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Top 10 Tech Trends for 2024 and Beyond: Innovations Shaping Our Future

Updated: Jun 25

Discover the top 10 tech trends for 2024 and beyond that will shape industries and improve our lives. Learn how these innovations, including Industry LLM, platform engineering, responsible AI, and more, will revolutionize technology and business practices.

Top 10 Tech Trends for 2024 and Beyond: Innovations Shaping Our Future

As we step into 2024, the technological landscape continues to evolve at an unprecedented pace, bringing groundbreaking innovations that promise to reshape industries and enhance our daily lives. At Trung Kiên Techdevinco, we are excited to share the top 10 tech trends for 2024 and beyond. These trends not only highlight the advancements in technology but also emphasize the need for responsible and sustainable development.


Top 10 Tech Trends for 2024 and Beyond: Innovations Shaping Our Future

An Industrial LLM (Large Language Model) is a specialized AI technology developed for industrial applications. Key points about Industrial LLMs:

  • Simplifying Complex Processes: They are designed to simplify complex industrial processes by offering new insights and reliable predictions, making them invaluable tools for improving manufacturing, maintenance, and process optimization.

  • Leveraging Extensive Datasets: Industrial LLMs leverage extensive industry-specific datasets and template libraries that include sensor data, events, asset information, and operational insights to provide precise, actionable intelligence.

  • Training with Historical Data: They are trained using real historical data and domain expertise, enabling them to offer expert-level insights and reasoning for crucial shopfloor tasks.

  • Integration and Accessibility: Industrial LLMs can operate independently through an API or be accessed through chatbots or role-based assistants. They seamlessly integrate with real-time data sources using a knowledge graph.

  • Applications: Key applications of Industrial LLMs include predictive maintenance, quality automation, production optimization, supply chain management, and custom design.

  • Optimizing Production: By leveraging the power of AI and machine learning, Industrial LLM applications can optimize production, improve quality control, enable predictive maintenance, and much more.

  • Adoption: The adoption of Industrial LLMs is accelerating as manufacturers realize their potential to drive efficiency, reduce costs, and improve product quality in a rapidly digitalizing industry.

In summary, Industrial LLMs are transformative AI technologies that are revolutionizing manufacturing by providing expert-level insights, automating complex processes, and enabling data-driven decision-making across the industry.



Platform engineering is a specialized discipline within software development that focuses on designing, building, maintaining, and improving the toolchains and workflows software developers use. Key aspects of platform engineering include:

  • Comprehensive Tools: Providing comprehensive and consistent tools and processes to enable developers to focus on software development instead of managing underlying toolchains.

  • Streamlining Development: Building a common suite of tools, services, and workflows that provides all development teams with an internal developer platform to streamline development, eliminate waste, and enhance efficiency.

  • Self-Service Tools: Empowering teams with self-service tools for deployment, containerization, testing, and more to help them build software faster and better.

  • Internal Developer Platform (IDP): Developing and maintaining infrastructure known as an internal developer platform (IDP) that lets developers do things in a self-service manner without filing tickets or waiting on specialists.

  • Product Management Principles: Treating the platform as an internal product by building and maintaining it using product management principles like user research, product roadmaps, soliciting feedback, iterating, launching, maintaining, and marketing.

Platform engineering teams are responsible for:

  • Designing, implementing, and maintaining the platform

  • Updating and upgrading the platform

  • Monitoring infrastructure

  • Supporting the platform and applications

  • Implementing self-service and automation

The key benefits of platform engineering include:

  • Improving developer productivity and efficiency

  • Reducing operational complexity and friction in the development process

  • Enabling self-service for developers to rapidly build, test, deploy, and monitor applications

  • Standardizing and securing key delivery processes

  • Setting and maintaining internal service level agreements

  • Monitoring team performance metrics

In summary, platform engineering is a transformative approach that simplifies complex development processes, enables developer self-service, and improves overall software delivery efficiency and reliability.


Platform engineering is a specialized discipline within software development that focuses on designing, building, maintaining, and improving the toolchains and workflows software developers use.
Platform engineering is a specialized discipline within software development that focuses on designing, building, maintaining, and improving the toolchains and workflows software developers use.

Key Principles of Responsible AI by Design

Responsible AI by Design is a critical approach to developing AI systems that prioritizes ethical considerations, transparency, and accountability throughout the entire lifecycle. The key principles include:

  1. Human Oversight and Governance: Establishing internal review boards and governance structures to ensure AI systems are developed and deployed responsibly.

  2. Fairness, Inclusiveness, and Prevention of Harm: Proactively identifying and mitigating potential biases or negative impacts on different stakeholder groups.

  3. Transparency and Explainability: Ensuring AI decision-making processes are documented and can be understood by both internal teams and end-users.

  4. Reliability, Safety, Security, and Respect for Privacy: Implementing robust safeguards to protect data integrity, user privacy, and the overall security of the AI system.

Putting Responsible AI into Practice

To implement Responsible AI by Design, organizations should take the following steps:

  1. Establish a Review Board: Create a cross-functional internal review board to oversee the development and deployment of AI systems.

  2. Engage Stakeholders: Identify and engage with stakeholders early and often to understand potential harms and address them.

  3. Embed Ethical Considerations: Infuse ethical considerations throughout the AI development lifecycle, including fairness testing and explainable features.

  4. Develop Rigorous Monitoring: Implement AI observability and monitoring practices to ensure ongoing responsible performance.

  5. Foster a Culture of Responsibility: Promote a culture that values ethical AI development and empowers teams to make responsible decisions.

Benefits of Responsible AI by Design

By adopting a Responsible AI by Design approach, organizations can:

  • Build trust with customers and stakeholders

  • Ensure ethical and equitable outcomes from AI systems

  • Mitigate risks of bias, privacy breaches, and unintended harm

  • Unlock the full transformative potential of AI technologies

  • Demonstrate leadership in the responsible development of AI

In summary, Responsible AI by Design is a critical framework for developing AI systems that prioritize ethical considerations, transparency, and accountability. By following these principles, organizations can harness the power of AI while ensuring it is deployed in a safe, secure, and trustworthy manner.


Responsible AI by Design is a critical approach to developing AI systems that prioritizes ethical considerations, transparency, and accountability throughout the entire lifecycle.
Responsible AI by Design is a critical approach to developing AI systems that prioritizes ethical considerations, transparency, and accountability throughout the entire lifecycle.

Software 2.0 refers to a new paradigm of software development where machine learning models, particularly deep learning, are used to generate code and solve problems, rather than traditional manual programming. Key points about Software 2.0:

  • Curating and Labeling Datasets: It shifts the focus from writing explicit code to curating and labeling large datasets that can train AI models to perform the desired tasks.

  • Automatic Code Generation: Software 2.0 leverages neural networks and other machine learning techniques to automatically generate code, rather than having developers write it line-by-line.

  • Efficiency and Scalability: This approach is more efficient and scalable than traditional "Software 1.0" development, as it can adapt to new data and requirements without extensive manual recoding.

  • New Skillsets: Software 2.0 requires new skillsets, with developers needing expertise in areas like data science, machine learning, and AI infrastructure, in addition to traditional programming.

  • Adoption Growth: Adoption of Software 2.0 is expected to grow as AI and machine learning capabilities advance, enabling more complex tasks to be automated through data-driven models rather than explicit code.

In summary, Software 2.0 represents a fundamental shift in software development, moving away from manual programming towards AI-generated code and solutions. This transition is expected to have significant impacts on the software industry and the skills required of developers.


Software 2.0 refers to a new paradigm of software development where machine learning models, particularly deep learning, are used to generate code and solve problems, rather than traditional manual programming.
Software 2.0 refers to a new paradigm of software development where machine learning models, particularly deep learning, are used to generate code and solve problems, rather than traditional manual programming.

What are Digital Humans?

Digital humans are human-like virtual beings that can represent real people in various digital scenarios. They are a subcategory of avatars, but go beyond traditional chatbots or digital assistants by combining lifelike appearance with the ability to communicate through verbal and nonverbal cues.

Key Characteristics of Digital Humans

  • Realistic Appearance: Digital humans are designed to look, act, and think like real humans, with features like facial expressions, gestures, and body language.

  • Conversational Abilities: They can engage in natural, human-like conversations, understanding context and meaning beyond just the words spoken.

  • Emotional Connection: Digital humans aim to foster an emotional connection with users, going beyond just providing information or services.

Applications of Digital Humans

Digital humans are being used in a variety of industries and use cases, including:

  • Customer Support: Serving as virtual customer service representatives to assist users.

  • Education and Training: Acting as instructors, tutors, or coaches to provide personalized learning experiences.

  • Healthcare: Providing virtual healthcare assistance, such as symptom diagnosis or medication guidance.

  • Entertainment: Appearing as digital characters or influencers in media and marketing.

  • Sales and Marketing: Engaging with customers to provide product recommendations or guide them through purchasing decisions.

Benefits of Digital Humans

  • Improved User Experience: Digital humans can create more natural, engaging, and personalized interactions compared to traditional chatbots or virtual assistants.

  • Scalability: They can provide human-like interactions at a much larger scale than in-person interactions.

  • Consistency: Digital humans can maintain a consistent personality and level of service across all interactions.

  • Data Collection: Interactions with digital humans can provide valuable data and insights about user behavior and preferences.

Challenges and Considerations

While digital humans offer many benefits, there are also challenges and ethical considerations to address, such as:

  • Uncanny Valley Effect: Ensuring digital humans appear realistic enough to be relatable, but not so realistic that they become unsettling.

  • Bias and Fairness: Ensuring digital humans do not perpetuate or amplify human biases.

  • Privacy and Data Security: Protecting user data and privacy in digital human interactions.

  • Transparency and Accountability: Clearly communicating the nature of the digital human interaction to users.

Overall, digital humans represent a transformative technology that is poised to significantly impact how we interact with technology and each other in the digital realm. As the technology continues to advance, it will be crucial to develop and deploy digital humans responsibly and ethically.


Digital humans are human-like virtual beings that can represent real people in various digital scenarios.
Digital humans are human-like virtual beings that can represent real people in various digital scenarios.

The Shift from Cloud to Edge Computing

Cloud computing has revolutionized how we store, process, and access data by enabling centralized computing resources delivered over the internet. However, as the number of connected devices and data generated at the network edge explodes, cloud computing alone is no longer sufficient. This has led to the rise of edge computing.

Key Differences Between Cloud and Edge Computing

  • Location of Processing: Cloud computing centralizes data processing in large data centers, while edge computing pushes intelligence and processing closer to the source of data at the network edge.

  • Latency: Edge computing reduces latency by processing data locally without the need to send it back to the cloud. This is critical for latency-sensitive applications.

  • Bandwidth: Edge computing minimizes the need to send large amounts of data to the cloud, reducing bandwidth requirements.

  • Reliability: Edge devices can continue operating even if connectivity to the cloud is lost, ensuring reliability.

Benefits of Moving from Cloud to Edge Computing

  • Lower Latency: Edge computing enables real-time processing and decision-making by reducing the distance data has to travel.

  • Reduced Bandwidth Costs: Processing data locally at the edge reduces the need to send large volumes of data to the cloud over expensive wide-area links.

  • Improved Reliability: Edge devices can operate autonomously even with intermittent cloud connectivity.

  • Enhanced Privacy and Security: Sensitive data can be processed and stored locally without traversing the public internet.

  • Scalability: Edge computing distributes processing across many devices, avoiding the need to scale up centralized cloud resources.

Challenges in Transitioning to Edge Computing

  • Distributed Management: Managing and orchestrating computing resources across many edge devices is more complex than centralized cloud management.

  • Heterogeneity: Edge devices are highly heterogeneous, requiring flexible software platforms to run on diverse hardware.

  • Security and Privacy: Securing and managing data privacy across many distributed edge locations is a significant challenge.

  • Interoperability: Ensuring seamless interoperability between edge devices, cloud platforms, and applications is critical but complex.

In summary, the transition from cloud to edge computing is driven by the need to process the massive amounts of data generated by IoT and mobile devices closer to the source. While cloud computing remains essential, edge computing complements it by enabling real-time processing, reduced bandwidth costs, and improved reliability and security. Overcoming the challenges of distributed management and heterogeneity will be key to realizing the full potential of edge computing.



Cloud computing has revolutionized how we store, process, and access data by enabling centralized computing resources delivered over the internet.
Cloud computing has revolutionized how we store, process, and access data by enabling centralized computing resources delivered over the internet.

What is Trust Architecture?

Trust Architecture is a conceptual framework used in various fields such as cybersecurity, information technology, and organizational management. It refers to the structural design and implementation of processes, technologies, and policies that foster trust within a system, network, or organization. The goal of Trust Architecture is to create a secure, reliable environment where interactions and transactions can be conducted with confidence in their integrity and authenticity.

Key Aspects of Trust Architecture

Implementing an effective Trust Architecture involves navigating several challenges:

  1. Establishing Secure Processes: Designing and implementing secure processes for authentication, authorization, and access control.

  2. Leveraging Enabling Technologies: Deploying technologies like encryption, digital signatures, and identity management to build trust.

  3. Defining Transparent Policies: Establishing clear, well-communicated policies around data handling, privacy, and compliance.

  4. Fostering a Culture of Trust: Promoting a culture that values transparency, accountability, and ethical behavior.

The Importance of Trust Architecture

Trust Architecture is foundational in today's interconnected and digitalized world. Its implementation across various domains underscores its importance in establishing secure, transparent, and reliable environments conducive to trust. As technologies and societal norms evolve, so too will the concepts and practices underpinning Trust Architecture, making it a dynamic and ever-relevant field.

Relationship to Zero Trust Architecture

Zero Trust Architecture (ZTA) is a specific approach to Trust Architecture that is gaining prominence. ZTA is based on the principle of "never trust, always verify" and focuses on:

  • Strong identity verification

  • Continuous device and user validation

  • Granular access controls

  • Threat monitoring and response

ZTA aims to address the limitations of traditional "castle and moat" security models by eliminating implicit trust and continuously validating every interaction.

In summary, Trust Architecture is a broader concept that encompasses the design and implementation of secure, reliable, and trustworthy systems, networks, and organizations. Zero Trust Architecture is a specific instantiation of Trust Architecture that is particularly well-suited for modern, cloud-based, and distributed computing environments.



Trust Architecture is a conceptual framework used in various fields such as cybersecurity, information technology, and organizational management.
Trust Architecture is a conceptual framework used in various fields such as cybersecurity, information technology, and organizational management.

Sustainable computing is a rapidly expanding field that focuses on developing computing technologies and practices that have a minimal impact on the environment. Key aspects of sustainable computing include:

  • Energy Efficiency: Designing hardware and software to minimize energy consumption and leverage renewable energy sources.

  • Circular Economy: Promoting the reuse, repair, and recycling of computing components to reduce waste and conserve natural resources.

  • Responsible Data Center Operations: Optimizing data center efficiency, water usage, and emissions to mitigate environmental impact.

  • Sustainable Software Engineering: Developing software with energy efficiency and environmental considerations in mind from the start.

  • Transparency and Reporting: Providing clear, consistent metrics and disclosures around the environmental impact of computing technologies.

The transition to sustainable computing requires collaboration across the entire technology ecosystem, from hardware manufacturers to software developers to data center operators. By adopting sustainable practices, the computing industry can play a key role in addressing global environmental challenges like climate change, resource depletion, and pollution.

Some of the most important research areas in sustainable computing include energy-efficient hardware design, green software engineering, sustainable data center management, and life cycle assessment of computing technologies. As the world becomes increasingly reliant on digital technologies, sustainable computing will only grow in importance in the years ahead.


Sustainable computing is a rapidly expanding field that focuses on developing computing technologies and practices that have a minimal impact on the environment.

Digital sovereignty refers to a nation's ability to exercise control over its own digital infrastructure, technologies, and data. It is becoming increasingly important as more aspects of our lives and economies become digitized, and as the use of digital technologies becomes a key factor in national security, economic development, and social welfare.

Key Aspects of Digital Sovereignty

  1. Data Protection and Privacy: The ability to protect the privacy and security of citizens' data through strong data protection laws and robust technical measures.

  2. Cybersecurity: The capability to defend against cyber threats through technical measures, governance frameworks, and international cooperation.

  3. Technological Innovation and Development: The ability to develop and control new technologies that are strategically important for national security and economic competitiveness.

  4. Openness and Interoperability: Ensuring that digital systems remain open and interoperable with those of other nations, while adhering to open standards and protocols.

  5. Digital Citizenship and Participation: Enabling citizens to access and use digital tools, participate in public debates around digital issues, and have their rights and freedoms protected in the digital realm.

Digital sovereignty is particularly important for Europe, which relies heavily on tech companies in the US and China to store and manage a significant amount of data. The European Union has taken steps to develop sovereign cloud solutions and local search engines to reduce its technological dependence on major transnational web players.

Achieving digital sovereignty is a complex challenge that requires collaboration across governments, businesses, and citizens. It involves striking a balance between maintaining control over digital assets and ensuring openness, interoperability, and innovation in the digital ecosystem.



What is the Augmented Workforce?

The augmented workforce refers to the integration of advanced technologies, such as artificial intelligence (AI), automation, and robotics, with human workers to enhance their capabilities and performance. This approach aims to leverage the complementary strengths of humans and machines to improve productivity, efficiency, and innovation in the workplace.

Key Characteristics of the Augmented Workforce

  1. Human-Machine Collaboration: The augmented workforce model emphasizes the collaboration between humans and intelligent technologies, where each contributes their unique strengths to accomplish tasks.

  2. Automation of Repetitive Tasks: Machines and automation are used to handle routine, repetitive, or dangerous tasks, freeing up human workers to focus on more complex, creative, and strategic work.

  3. Enhanced Human Capabilities: Technologies like augmented reality (AR), wearable devices, and AI-powered tools are used to augment human workers, providing them with real-time information, guidance, and decision support.

  4. Continuous Learning and Upskilling: The augmented workforce model requires employees to continuously learn and develop new skills to adapt to the changing technological landscape and work alongside intelligent systems.

  5. Increased Efficiency and Productivity: By combining human expertise with the speed and precision of machines, the augmented workforce can drive significant improvements in operational efficiency, quality, and overall productivity.

Benefits of the Augmented Workforce

  1. Improved Employee Engagement and Satisfaction: By offloading repetitive tasks to machines, employees can focus on more meaningful and fulfilling work, leading to increased job satisfaction and engagement.

  2. Enhanced Safety and Well-being: Automation can help reduce the risk of injuries and accidents, particularly in hazardous or physically demanding work environments.

  3. Increased Flexibility and Adaptability: The augmented workforce model allows organizations to quickly adapt to changing market conditions and customer demands by leveraging the capabilities of both humans and machines.

  4. Accelerated Innovation and Problem-Solving: The combination of human creativity and machine intelligence can lead to the development of innovative solutions and the ability to tackle complex problems more effectively.

  5. Improved Training and Knowledge Transfer: Technologies like AR and AI-powered learning platforms can enhance employee training and knowledge sharing, enabling faster onboarding and skill development.

Challenges and Considerations

  1. Workforce Displacement and Reskilling: The integration of automation and AI may lead to the displacement of some jobs, requiring organizations to invest in reskilling and upskilling programs to help workers adapt.

  2. Ethical and Regulatory Concerns: The use of advanced technologies in the workplace raises ethical questions around privacy, bias, and the impact on human workers that must be addressed.

  3. Organizational Change Management: Transitioning to an augmented workforce model requires significant organizational change, which can be challenging to manage and implement effectively.

  4. Technology Integration and Interoperability: Ensuring seamless integration and interoperability between various technologies and systems is crucial for the successful implementation of the augmented workforce.

As the pace of technological change accelerates, the augmented workforce model is becoming increasingly important for organizations to remain competitive, innovative, and adaptable in the face of evolving market demands and workforce dynamics.


The augmented workforce refers to the integration of advanced technologies, such as artificial intelligence (AI), automation, and robotics, with human workers to enhance their capabilities and performance.
The augmented workforce refers to the integration of advanced technologies, such as artificial intelligence (AI), automation, and robotics, with human workers to enhance their capabilities and performance.

Embrace the Future with Trung Kiên Techdevinco

At Trung Kiên Techdevinco, we are committed to staying at the forefront of these technological advancements. We believe that by embracing these trends, we can drive innovation and create solutions that meet the needs of today while anticipating the demands of tomorrow. Join us as we explore the exciting possibilities that 2024 holds and beyond.

By focusing on these trends, businesses can position themselves to take advantage of new opportunities and stay competitive in an ever-evolving digital landscape. Stay tuned to our blog for more insights and updates on the latest in technology.


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Below are reference links to supplement the article on technology trends 2024:

  1. SymphonyAI - Industrial LLM

  2. Ridgeant - LLM in Manufacturing

  3. Vanti - How LLM Applications are Revolutionizing the Manufacturing Industry

  4. XenonStack - Large Language Model in Manufacturing Industry

  5. TechTarget - What is Platform Engineering?

  6. Puppet - Platform Engineering

  7. Platform Engineering - What is Platform Engineering

  8. Microsoft - What is Platform Engineering

  9. CircleCI - What is Platform Engineering

  10. Enterprisers Project - Responsible AI by Design

  11. Orange Matter - Introducing AI by Design Principles for Responsible AI

  12. Responsible BioDesign

  13. IEEE Xplore - Responsible AI by Design

  14. Computer - Responsible AI by Design

  15. LinkedIn - Software 2.0: Future of Programming

  16. KLU - Software 2.0

  17. Clarifai - All You Need to Know About Software 2.0

  18. Softtek - Software 2.0: An Emerging Era of Automatic Code Generation

  19. LinkedIn - Software 2.0

  20. Synthesia - Digital Humans

  21. Digital Humans

  22. Unreal Engine - Digital Humans

  23. Digital Humans

  24. We Are Digital Humans

  25. HPE - What is Edge-to-Cloud?

  26. ENTSO-E - Cloud and Edge Computing

  27. Akamai - What is Cloud Edge Computing?

  28. CableLabs - Moving Beyond Cloud Computing to Edge Computing

  29. Viettel IDC - Edge Cloud Computing

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