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Surprising trends in India’s digital content consumption

4 minutes, 35 seconds read

In a country that ranks second in the world for video consumption, cheap data is often attributed as the primary driver behind it. Although data is cheapest in India (Rs. 18.5/GB in 2018, Rs. 3.4/GB in 2019), regional content curated and consumed by natives contributed a great deal to the adoption of digital in rural India. Digital content consumption is expected to double, with over a billion of the population having a smartphone by the next decade. Let’s see what will change in the coming decade? But before, a quick insight into the existing Indian digital landscape.

India’s Digital Demography

Users: 94% of the urban population in India has an internet subscription; which falls to a considerable low among the rural populace (only 24%), according to TRAI.

There are four categories of internet users – Digital sophisticates (3%): these are tech-savvy, wealthy, and urban and prefer global and original content; Digital enthusiasts (36%): these are mainly smartphone & TV streaming users with preference for Hindi and regional content; Digital mainstream (59%): these are predominantly smartphone users and seek free content available online or bundled TV packages; Fringe users (2%): these are irregular users belonging to remote areas where internet connectivity is poor. (India’s Digital Future, KPMG, 2019)

Temp-infographic

Preferences: Nearly 30% of google search in India is voice-driven (Business Standard, 2019), indicating voice assistance will further progress linguistic democratization.

In India, YouTube accounts for nearly 265 million unique, active users. 95% of these users watch videos in their regional languages (Economic Times, 2018).

Google and Facebook account for nearly 80% of the digital advertisement in India (KPMG India analysis). In 2018, Google reported INR 93 billion in revenues from its operations in India, with 67% accruing from its digital ads platform. Also, video ads contribute to most of ad-spent (53%).

In 2018, there were 340 million smartphone users in India, which is projected to reach 829 million by 2022, according to the CISCO VNI report.

New Trends in Digital Content Consumption

Today, video streaming services have more subscribers (613 million) than traditional cable connection (556 million), according to VentureBeat news.

The media consumption in India has grown at a CAGR 9% during 2012-18 (IBEF, 2019), which is almost nine times that of the US. Print media and television remains the largest platform for advertisement, however the future might witness a shift.

The Indian FMCG sector spends the most on digital advertising. However, considering its overall budget, it’s only 16%. Interestingly, the BFSI sector spends nearly 38% of its marketing budget on digital advertising. (Dentsu Aegis Digital Report, 2019) This indicates that industries have started to realize and invest in digital platforms.

Regional content: According to KPMG in India analysis, consumers spend 35-43% of their time on regional videos on digital platforms. Digital content and media platforms like Zee5, Hotstar, Voot, and Amazon Prime Video are keen on producing original and region-based content. According to Financial Express, the cost to develop regional content is 30-40% lower than that of Hindi and has a larger viewership. 

Original content: The increased digital content consumption also demands originality. Today, content generation is not limited to the media and entertainment industry. For instance, in September 2019, Zomato launched a video streaming service on its app. The primary goal remains the same- customer engagement. Addressing the fact that food is not the only thing people consume these days, businesses are penetrating the minds of youth through quality and original content. 

Hotstar reports 80% of its viewership from dramas and movies and plans to invest INR 120 crores in creating original content.

The Future of Content in India and APAC

The next significant disruption in content consumption will come from 5G technology. Because digital content needs internet and India’s still dangling between 2G and lower cap of the 4G network. Setting up a 5G network will require a $500 billion investment in the next 5-7 years. The government is expecting the initial deployment of the 5G network by 2020 and roll-out by 2022.

5G technology will be able to handle more traffic at a higher speed, satisfying the demand for high data and the growing number of mobile users. HD content will become a thing of the past and consumers will be interacting with augmented reality in their everyday life. It will not only enhance augmented reality and virtual reality experiences but will also support IoT, autonomous vehicles, and automation to name a few. However, India isn’t quite ready for 5G technology yet. The following graph illustrates the countries which are about to enter the 5G era.

5G-Adoption-across-the-world

An overview of digital behavior in Japan, Korea, and Singapore which are among the top 10 countries to deploy 5G.

 JapanRepublic of KoreaSingapore
Internet penetration93%99.5%84.0%
Mobile penetration89.9%95.8%147.3%
Preferred device to go onlineSmartphone (59.7%)Smartphone (94.3%)
Online activityEmail (80.2%), weather report (65.8%), transport (63.4%)Communication (95.2%), information search (94.0%)

Source: SourceSource: India’s Digital Future, KPMG

5G will also make technologies like Augmented Reality, Virtual Reality, cloud-based gaming, IoT and OTT services commercially available.

Apart from this, AI (Artificial Intelligence) will continue to retain customer engagement through predictive analytics, machine learning, and natural language processing capabilities.

For example, Hotstar uses machine learning algorithms for personalized movie recommendations. It predicts user preferences by calculating total watch time per user per month. The company is leveraging AI technology for translations, audio to text conversions, video compression, object detection, and scene classification.

Also read – Your Shopping Cart just got a whole lot Smarter, this festive season.

We’re an AI-first products and solutions firm with extensive experience in insurance and consumer internet domains. Feel free to reach us out at hello@mantralabsglobal.com for an intelligent digital solution to your business requirements.

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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