Artificial Intelligence & Machine Learning
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Next-Generation Technologies & Secure Development
Could Open-Source AI Redefine the Future? Here’s What Experts Say

The artificial intelligence industry is undergoing transformation, with open-source models challenging the dominance of proprietary AI. The recent launch of DeepSeek-R1, an open-source large language model from China, has intensified discussions around efficiency, accessibility and the future of AI monetization.
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Experts claim DeepSeek-R1’s performance competes with OpenAI’s top-tier models in reasoning tasks such as mathematical computation and coding while operating at 2% of the cost. DeepSeek has also made its model weights publicly available – disrupting traditional AI revenue models and pushing the industry toward a more open ecosystem. The lower costs of training and inference mean that researchers can perform many more experiments. Andrej Karpathy, one of the engineers involved with DeepSeek, has suggested establishing a global “RL-gym” to create a wide range of RL environments to understand how LLMs think and make decisions.
“AI is entering a new era where efficiency, cost-effectiveness and strategic deployment matter more than computing power,” said Eric Schmidt, former CEO and chairman of Google.
Open Source: A Boon or Bane?
“This is no longer just a technological race, it’s a geopolitical one. While open-source models offer accessibility, their full training pipeline and datasets often remain undisclosed. Nations are using AI to influence global markets, trade policies and digital sovereignty,” said Amitkumar Shrivastava, global distinguished engineer and head of AI at Fujitsu Consulting India. “The real winners will be those who balance innovation with regulatory foresight and ethical AI practices.”
While open-source AI fosters innovation, it also raises concerns about security, compliance and ethical risks. Increased accessibility introduces challenges such as misinformation, deepfake generation and unauthorized automation.
“DeepSeek is open-source, which is very important, as it allows users to download the models and run them on their own hardware if they have the capacity. We are already seeing others create local installations of DeepSeek models even without GPUs,” Professor Balaraman Ravindran, IIT Madras, wrote in his blog.
“Assuming that DeepSeek’s claims on infrastructure reductions are true, some researchers are still not fully convinced and are in the process of verifying the claims. There will be an immediate breakdown of the monopolistic hold of a few technology giants with deep pockets to control the AI market – much like India developing cheap Corona vaccine,” said Dr. Sanjeev Kumar, chief AI and digital officer, Wadhwani Centre for Government Digital Transformation, Wadhwani Foundation.
The Hybrid Model: A Middle Ground?
As open-source AI gains traction, proprietary AI companies are rethinking their strategies that balance control and accessibility. Some are adopting hybrid models that combine open-source elements with enterprise offerings. DeepSeek’s business model shows this shift, providing free access to distilled versions – 1.5 billion to 70 billion parameters while charging for full-scale API access directly competing with OpenAI and Anthropic.
Industry leaders are already adapting to this new reality. “We may see AI providers shift their focus from broad consumer AI to B2B and enterprise automation,” said Ankush Sabharwal, founder and CEO of CoRover.ai, which developed BharatGPT, India’s first indigenous open-source LLM. “Companies will need to differentiate through domain-specific models, proprietary data integration and specialized AI services.”
AI’s Global Power Struggles: The Bigger Picture
DeepSeek’s rise underscores the increasing geopolitical significance of AI. The U.S. has reinforced its AI leadership with the $500 billion Stargate AI project, but China is proving that efficient AI architectures can rival even the well-funded initiatives. India and the EU accelerate innovation to stay competitive.
In response to global developments, the IndiaAI Mission has launched an initiative to develop indigenous foundational AI models, including LLMs and SLMs. This framework addresses region-specific challenges across sectors such as healthcare, education, agriculture, climate and governance.
Efficiency Over Scale: A New AI Shift
DeepSeek’s success demonstrates that AI breakthroughs no longer require billion-dollar infrastructure. “This model proves that optimization through reinforcement learning, knowledge distillation and efficient architecture can deliver high performance at a fraction of the cost,” said Jibu Elias, responsible computing challenge fellow at Mozilla Foundation.
This trend could reshape AI investment strategies. Instead of pouring billions into massive AI clusters, venture capitalists may shift their focus to lean AI startups prioritizing efficiency. The demand for AI-optimized chips and custom accelerators could redefine the semiconductor market, emphasizing energy efficiency over raw computational power.
“The shift from pilot to production is happening fast, but the real challenge isn’t just adopting AI, it’s about making the right architecture choices and building applications that deliver outcomes,” Sanjay Srivastava, chief digital officer at Genpact, wrote in his blog.
What’s Next?
DeepSeek’s rapid rise signals a broader trend toward smaller, cost-efficient and task-optimized AI models rather than simply chasing larger-scale architectures. This shift is expected to accelerate AI-as-a-service adoption, where businesses subscribe to AI capabilities rather than owning and maintaining large-scale models.
This market transformation comes with regulatory challenges. Governance frameworks, including the EU AI Act, the U.S. AI Executive Order and China’s AI policies, aim to address these complexities.
DeepSeek’s disruption isn’t just about open-source AI, it’s about the future direction of AI itself. Efficiency, accessibility and innovation are driving a paradigm shift in how AI is built, deployed and monetized.
“Meta’s commitment to open-source AI empowers organizations to refine these tools, enabling a scalable approach to AI adoption across industries. This democratization allows organizations to build trusted AI environments without proprietary constraints,” said Yann LeCun, chief AI scientist at Meta.
Whether AI firms embrace or resist the transformation, one thing is clear: The AI industry will never be the same again.