THE year 2025 marks a defining moment in the AI revolution as the technology transitions from an exclusive domain of deep-pocketed corporations to a force that is reshaping industries, economies and global power dynamics.
Once confined to high-cost proprietary models like OpenAI’s GPT and Google’s DeepMind, the AI landscape is now witnessing an unprecedented disruption driven by open-source innovation, cost-effective models and new challengers that are reshaping accessibility.
A standout example is DeepSeek, a model
that has rapidly gained attention by delivering high-performance AI capabilities at a fraction of the cost of mainstream alternatives.
By leveraging low-tech hardware requirements and an open-source approach, DeepSeek has shattered the notion that
cutting-edge AI development is reserved for industry giants.
This paradigm shift not only lowers the entry barrier for AI adoption but also offers new opportunities for startups, small businesses and developing nations to participate in AI innovation.
DeepSeek is not alone in this transformation. Other models like Mistral (France) and Llama (Meta) are also pushing the boundaries of customisable, transparent and democratised AI solutions.
Meanwhile, efficiency-driven models, such as Falcon (Technology Innovation Institute), are proving that AI can deliver high computational power with lower resource consumption.
The implications of this democratisation of
AI extend far beyond technology. It challenges the dominance of traditional AI powerhouses, shifts the balance of AI governance and raises critical questions about the role of AI in
economic development, ethical stewardship and international competition.
The focus is no longer solely on which AI model is the most powerful but rather on how AI can be developed responsibly, equitably and with global inclusivity in mind.
As we step into this new era, the challenge for governments, businesses and society is not just keeping up with AI’s rapid advancements but ensuring that its growth is guided by ethical governance, transparency and sustainability.
The choices we make today will determine whether AI remains a tool for the privileged few or becomes a transformative force for all of humanity.
AI ecosystem: Key contenders
The AI landscape is a dynamic ecosystem composed of diverse models with distinct capabilities. Below are some of the leading contenders shaping AI’s future:
Conversational AI models
ChatGPT (OpenAI): A leader in conversational AI, excelling in tasks from
creative writing to customer service, with ongoing challenges in cost management and contextual coherence.
Claude 3.5 (Anthropic): Prioritises safety
and ethical considerations, making it ideal for responsible AI applications, though its customisation options remain limited.
Multimodal and specialised models
Gemini 2.0 (Google DeepMind): Processes text, images and video with high proficiency but struggles with real-time adaptability.
GitHub Copilot: Enhances developer productivity with real-time code generation but raises concerns over potential bias in training data.
Open-source innovators
DeepSeek (China): A cost-effective and open-source model democratising AI, though challenges remain in accuracy and bias mitigation.
Llama (Meta): A customisable model empowering researchers, with ongoing efforts to enhance multimodal capabilities.
Mistral (France): Committed to open-source collaboration, though securing competitive funding remains a challenge.
Global and real-time models
Qwen 2.5 Max (Alibaba Cloud): Excels in multilingualism, with support for over 100 languages but faces challenges in global expansion.
Bard and PaLM 2 (Google): Provides real-time information via Google search but struggles with misinformation and accuracy.
Grok (xAI by Elon Musk): Integrated with X (formerly Twitter), offering unfiltered real-time responses, though its reliance on social media raises concerns over bias.
Efficiency-focused models
Falcon (Technology Innovation Institute): Known for its efficiency, delivering high performance with lower computational costs, though enterprise adoption requires greater transparency.
Navigating AI maze: Choosing the right tool
Selecting the right AI model depends on specific requirements:
Global businesses: Qwen 2.5 max
Resource-constrained developers: DeepSeek
General conversations: ChatGPT
Multimodal applications: Gemini 2.0
Safety-critical applications: Claude 3.5
Software development: GitHub Copilot
Customisation and research: Llama
Real-time information: Bard and PaLM 2
Efficiency: Falcon
Open-source collaboration: Mistral
Unfiltered real-time data: Grok
AI arms race: Global perspective
AI development has evolved into a geopolitical contest, with key players shaping the landscape:
United States
Home to OpenAI and Google DeepMind, the US leads in AI innovation, particularly in natural language processing and machine learning. However, concerns over ethical oversight and corporate dominance remain.
China
China prioritises AI self-reliance, leveraging state-backed initiatives in quantum computing and automation. However, there are international concerns over data privacy and surveillance impact trust in Chinese AI models.
European Union
The EU focuses on ethical AI, implementing robust regulations like the AI Act to balance innovation with societal safeguards, ensuring trustworthy AI development.
India
India is emerging as a global AI hub, leveraging a strong talent pool in data science and software engineering. Government initiatives, such as the National AI Strategy, are fostering AI innovation. However, challenges remain in data accessibility and infrastructure development.
Malaysia
Malaysia is positioning itself as a key player in Southeast Asia’s AI landscape. Government initiatives, including the National AI Office (NAIO) established on Aug 28, 2024, underscore the nation’s commitment to AI-driven growth.
Malaysia has the potential to accelerate its
AI progress by leveraging open-source and
cost-effective models like DeepSeek, which democratises AI development. Additionally, AI applications in fintech, healthcare and smart governance can help position Malaysia as a regional AI leader.
Future of AI: Challenges and opportunities
The trajectory of AI will be shaped by how we address key challenges and embrace emerging trends:
Ethical AI development
Mitigating bias to ensure fairness and prevent discrimination.
Enhancing transparency and explainability in AI decision-making.
Strengthening privacy and data security to prevent misuse.
Regulation vs innovation
Balancing regulation with innovation to foster responsible AI.
Establishing clear frameworks that ensure ethical AI without stifling progress.
Democratising AI
Ensuring AI accessibility across all societal sectors.
Addressing AI-driven socio-economic inequalities.
Emerging trends
Efficiency and sustainability: Developing AI with a reduced environmental impact.
Multimodality: Enhancing AI-human interaction across text, image and video.
Open-source growth: Encouraging global collaboration.
Cross-technology integration: AI converging with quantum computing, biotechnology, and cloud computing.
The AI landscape of 2025 is evolving at an unprecedented pace, driven by technological advancements, geopolitical competition and ethical considerations.
Emerging players like India and Malaysia are contributing to this transformation, leveraging their unique strengths to carve a niche in AI development.
However, true progress hinges not just on innovation but on ensuring AI serves humanity responsibly.
By prioritising ethical development, responsible governance and inclusive access, we can shape AI into a force for global progress. The choices we make today will define AI’s role in shaping the future. Now is the time to act decisively to ensure AI benefits all of humanity.
Ts Dr Manivannan Rethinam is a distinguished professional technologist and holds a doctorate in Business Administration, with a focus on marketing and technology management. Comments: letters@thesundaily.com
