The Game-Changer: DeepSeeks’ Low-Cost, High-Performance AI Model
The DeepSeek-V3 model, recently unveiled by the Chinese AI startup DeepSeek, is a game-changer for the AI industry. By employing a Mixture-of-Experts (MoE) architecture, DeepSeek has achieved performance levels comparable to OpenAI’s GPT-4o model while significantly reducing development and operational costs. DeepSeek-V3 was developed for just USD 5.6 million, whereas GPT-4o is estimated to have cost hundreds of millions of dollars. This demonstrates that both cost efficiency and technological innovation will be key competitive factors in the AI market.
The rise of DeepSeek represents more than just a new technological advancement; it signals a fundamental structural shift in the AI industry. Traditional AI models require costly GPU infrastructure for training and inference, but DeepSeek has disrupted the market with a strategy centered on maximizing computational efficiency and reducing costs. This move has accelerated the overall decline in the cost of AI services.
As AI technology advances, the cost of developing and operating AI models has dropped significantly, lowering barriers to entry in the industry. This has encouraged the emergence of diverse AI-driven business models and spurred rapid change across the AI ecosystem.
Innovative Services Emerge as AI Model Development Costs Fall
In the past, large technology companies dominated the AI market due to high development costs. However, with the advent of more affordable and powerful AI models like DeepSeek, startups are entering the market with innovative services. As AI development becomes less expensive, companies of various sizes can build, operate, and monetize AI-based services. As a result, the AI industry has entered a new era marked by both diversity and intense competition.
The falling cost of AI also has broad implications for the AI SaaS (Software as a Service) market. Historically, major cloud providers such as OpenAI, Google Cloud, and Microsoft Azure have ruled the market by offering AI APIs. Now, as it becomes cheaper to run AI models, more companies are looking to develop and operate their own. Consequently, existing AI SaaS providers must explore new revenue models beyond selling AI APIs—such as AI model customization and dataset management solutions—in order to remain competitive.
Lower AI costs are vital to accelerating innovation in the industry and making AI technologies more accessible. AI will be deployed in increasingly diverse fields, and AI-based business models will evolve rapidly. As AI becomes more ubiquitous, organizations must differentiate themselves not merely by capital but by constant technological innovation. They must also prepare for a new phase of technology protection and more vigorous competition, stepping away from the collaborative relationships built on open technology sharing. To seize the opportunities arising from the democratization of AI, companies need flexible, innovative business models that foster continued growth.
AI Technology Moves From Cooperation to Competition
Historically, AI technology has advanced through widespread disclosure and sharing—stemming from an archival culture and open-source ethos. Notably, OpenAI was originally founded as a nonprofit organization committed to openness and sharing its technology to further the progression of AI.
AI development has been marked by high costs and uncertain commercial returns, which encouraged a spirit of collaboration and knowledge exchange. When confidence in the profitability of AI was low, cooperative research and development were common, reflecting the relatively nascent stage of AI at that time. Governments and companies invested heavily in AI, prioritizing long-term technological potential over immediate revenue.
However, this dynamic changed dramatically when AI development costs plummeted, opening the door to monetizable AI-based services. AI technology was no longer something to be shared freely but became a fiercely competitive arena. Since releasing GPT-4, OpenAI has adopted a far more “closed” strategy, choosing not to publicly release models, source code, datasets, or detailed training information.
As AI development grows more affordable, enabling more organizations to create and deploy their own AI models, we are witnessing a shift from technology disclosure and collaboration to technology protection and competition. This marks a significant departure from previous norms and underscores that companies with a technological edge, rather than just deep pockets, will drive future progress.
OpenAI’s Attitude Change 1 – Securing Patent Rights
[OpenAI’s Position on Patents, Source: Open AI]
OpenAI, which had historically avoided patenting its technology, began actively seeking patents in 2023, signaling a marked shift from its earlier stance. Although the company maintains it will use these patents for defensive purposes only, the new strategy underscores a clear change in policy.
[Figure 2: OpenAI is actively pursuing patent rights starting in 2023]
According to these patents, OpenAI focuses on a broad array of AI technologies centered on language models, encompassing text generation, editing, insertion, text-based image generation, natural language processing (NLP), and text-based API integration. The scope also extends to multimodal machine learning models that combine text and images, enabling the system to process diverse data, including audio data and speech. This expansion has accelerated progress in AI technologies that can understand and process a variety of data formats.
Furthermore, OpenAI is contributing to software development automation and improved coding accessibility through code-generation technology that produces computer code from natural language instructions. Reinforcement learning techniques are being applied to train and enhance machine learning models, improving decision-making and self-learning capabilities. Additionally, OpenAI is developing adaptive user interface (UI) technologies that adjust interfaces based on user inputs, thereby enhancing user experience.
The patents indicate that OpenAI is extending its language model to process multiple data types—text, images, and audio—while continuing research into data normalization and data quality improvement. By integrating natural language understanding and code-generation technologies, OpenAI aims to streamline human-computer interactions. Concurrently, it is optimizing the user experience through the development of personalized AI assistants (“MyAi”) and adaptive UIs. The company also employs its machine learning models to automate tasks in data labeling, action automation, and software development.
OpenAI’s Attitude Change 2 – Raising IP Issues
OpenAI has accused DeepSeek of infringing its intellectual property rights, alleging that DeepSeek illicitly extracted data from OpenAI’s AI models and used this data to develop its own model through a process known as “distillation.” Distillation, which involves transferring knowledge from larger models to smaller ones, is widely used in AI research. Nonetheless, it has recently become controversial as a potential channel for unauthorized technology acquisition.
OpenAI contends that DeepSeek may have violated its Terms of Service and is considering legal action. Microsoft’s security research team uncovered evidence suggesting that individuals linked to DeepSeek accessed large volumes of data through OpenAI’s APIs, which OpenAI believes indicates DeepSeek’s model may have been developed using GPT-4.
DeepSeek has categorically denied these claims, insisting that its research is based on an open-source model. Given that GPT-4 is not publicly available, DeepSeek argues that it could not have been stolen and attributes the allegations to commercial or geopolitical rivalries. The company emphasizes the substantial computational cost and data-refinement work underlying its proprietary technology, noting that even its own employees do not have full knowledge of the training data.
This case underscores the importance of balancing AI technology development with protecting intellectual property rights. While AI is advancing rapidly, the discourse around potential IP infringement is still at an early stage. The DeepSeek incident highlights the complex legal and ethical challenges associated with AI development, revealing the need to reconcile technology sharing with IP protection for the sustainable growth of the AI field.
Increased Competition in AI Technology and the Importance of a Patent Strategy
As AI technology advances at a rapid pace and competition in the AI services market intensifies, AI technology patents are emerging as vital assets for achieving a competitive edge. The rise of generative AI has underscored the importance of examining patents related to AI services. With the growing availability of low-cost, high-performance AI models like DeepSeek reducing development expenses, the race to secure AI patents for technological leadership will only become more competitive.
Notably, even major tech companies such as OpenAI—which once maintained more open policies—are increasingly determined to protect their innovations by pursuing patents. This reflects the broader reality of escalating competition in AI and the heightened importance of safeguarding technological advances. The recent allegations of OpenAI’s technology being misappropriated by DeepSeek illustrate this trend vividly.
For those conducting business in the AI sphere, it is no longer sufficient to analyze patents from service-oriented providers alone (e.g., Palantir). It has become essential to examine the patents of foundational model developers like OpenAI and Google. While AI historically thrived on open-source contributions and archival data, the advent of business models with viable returns—and the corresponding drop in development costs—have shifted the landscape. Companies must now not only invest aggressively in AI technology but also shield their intellectual property through robust patent strategies to stay competitive.
AI innovations have the power to create groundbreaking services and drive societal progress, but the safeguarding of intellectual property and the ethical development of AI cannot be overlooked. To succeed in the increasingly fierce AI market and foster ongoing innovation, businesses need both solid technological foundations and well-considered patent strategies.
#PatentLaw #OpenAI #DeepSeek #MoE #AIModels #TechDisruption #ArtificialIntelligence #AIInnovation #TechTrends #MachineLearning #DeepLearning #AIResearch #AICompetition #IntellectualProperty #AIPatents #AIRegulations #TechProtection #AITransformation #AIEconomy #GenerativeAI #AIFuture #AIIndustry #BusinessStrategy #StartupGrowth #Innovation #DigitalTransformation #FutureOfTech
The Game-Changer: DeepSeeks’ Low-Cost, High-Performance AI Model
The DeepSeek-V3 model, recently unveiled by the Chinese AI startup DeepSeek, is a game-changer for the AI industry. By employing a Mixture-of-Experts (MoE) architecture, DeepSeek has achieved performance levels comparable to OpenAI’s GPT-4o model while significantly reducing development and operational costs. DeepSeek-V3 was developed for just USD 5.6 million, whereas GPT-4o is estimated to have cost hundreds of millions of dollars. This demonstrates that both cost efficiency and technological innovation will be key competitive factors in the AI market.
The rise of DeepSeek represents more than just a new technological advancement; it signals a fundamental structural shift in the AI industry. Traditional AI models require costly GPU infrastructure for training and inference, but DeepSeek has disrupted the market with a strategy centered on maximizing computational efficiency and reducing costs. This move has accelerated the overall decline in the cost of AI services.
As AI technology advances, the cost of developing and operating AI models has dropped significantly, lowering barriers to entry in the industry. This has encouraged the emergence of diverse AI-driven business models and spurred rapid change across the AI ecosystem.
Innovative Services Emerge as AI Model Development Costs Fall
In the past, large technology companies dominated the AI market due to high development costs. However, with the advent of more affordable and powerful AI models like DeepSeek, startups are entering the market with innovative services. As AI development becomes less expensive, companies of various sizes can build, operate, and monetize AI-based services. As a result, the AI industry has entered a new era marked by both diversity and intense competition.
The falling cost of AI also has broad implications for the AI SaaS (Software as a Service) market. Historically, major cloud providers such as OpenAI, Google Cloud, and Microsoft Azure have ruled the market by offering AI APIs. Now, as it becomes cheaper to run AI models, more companies are looking to develop and operate their own. Consequently, existing AI SaaS providers must explore new revenue models beyond selling AI APIs—such as AI model customization and dataset management solutions—in order to remain competitive.
Lower AI costs are vital to accelerating innovation in the industry and making AI technologies more accessible. AI will be deployed in increasingly diverse fields, and AI-based business models will evolve rapidly. As AI becomes more ubiquitous, organizations must differentiate themselves not merely by capital but by constant technological innovation. They must also prepare for a new phase of technology protection and more vigorous competition, stepping away from the collaborative relationships built on open technology sharing. To seize the opportunities arising from the democratization of AI, companies need flexible, innovative business models that foster continued growth.
AI Technology Moves From Cooperation to Competition
Historically, AI technology has advanced through widespread disclosure and sharing—stemming from an archival culture and open-source ethos. Notably, OpenAI was originally founded as a nonprofit organization committed to openness and sharing its technology to further the progression of AI.
AI development has been marked by high costs and uncertain commercial returns, which encouraged a spirit of collaboration and knowledge exchange. When confidence in the profitability of AI was low, cooperative research and development were common, reflecting the relatively nascent stage of AI at that time. Governments and companies invested heavily in AI, prioritizing long-term technological potential over immediate revenue.
However, this dynamic changed dramatically when AI development costs plummeted, opening the door to monetizable AI-based services. AI technology was no longer something to be shared freely but became a fiercely competitive arena. Since releasing GPT-4, OpenAI has adopted a far more “closed” strategy, choosing not to publicly release models, source code, datasets, or detailed training information.
As AI development grows more affordable, enabling more organizations to create and deploy their own AI models, we are witnessing a shift from technology disclosure and collaboration to technology protection and competition. This marks a significant departure from previous norms and underscores that companies with a technological edge, rather than just deep pockets, will drive future progress.
OpenAI’s Attitude Change 1 – Securing Patent Rights
[OpenAI’s Position on Patents, Source: Open AI]
OpenAI, which had historically avoided patenting its technology, began actively seeking patents in 2023, signaling a marked shift from its earlier stance. Although the company maintains it will use these patents for defensive purposes only, the new strategy underscores a clear change in policy.
[Figure 2: OpenAI is actively pursuing patent rights starting in 2023]
According to these patents, OpenAI focuses on a broad array of AI technologies centered on language models, encompassing text generation, editing, insertion, text-based image generation, natural language processing (NLP), and text-based API integration. The scope also extends to multimodal machine learning models that combine text and images, enabling the system to process diverse data, including audio data and speech. This expansion has accelerated progress in AI technologies that can understand and process a variety of data formats.
Furthermore, OpenAI is contributing to software development automation and improved coding accessibility through code-generation technology that produces computer code from natural language instructions. Reinforcement learning techniques are being applied to train and enhance machine learning models, improving decision-making and self-learning capabilities. Additionally, OpenAI is developing adaptive user interface (UI) technologies that adjust interfaces based on user inputs, thereby enhancing user experience.
The patents indicate that OpenAI is extending its language model to process multiple data types—text, images, and audio—while continuing research into data normalization and data quality improvement. By integrating natural language understanding and code-generation technologies, OpenAI aims to streamline human-computer interactions. Concurrently, it is optimizing the user experience through the development of personalized AI assistants (“MyAi”) and adaptive UIs. The company also employs its machine learning models to automate tasks in data labeling, action automation, and software development.
OpenAI’s Attitude Change 2 – Raising IP Issues
OpenAI has accused DeepSeek of infringing its intellectual property rights, alleging that DeepSeek illicitly extracted data from OpenAI’s AI models and used this data to develop its own model through a process known as “distillation.” Distillation, which involves transferring knowledge from larger models to smaller ones, is widely used in AI research. Nonetheless, it has recently become controversial as a potential channel for unauthorized technology acquisition.
OpenAI contends that DeepSeek may have violated its Terms of Service and is considering legal action. Microsoft’s security research team uncovered evidence suggesting that individuals linked to DeepSeek accessed large volumes of data through OpenAI’s APIs, which OpenAI believes indicates DeepSeek’s model may have been developed using GPT-4.
DeepSeek has categorically denied these claims, insisting that its research is based on an open-source model. Given that GPT-4 is not publicly available, DeepSeek argues that it could not have been stolen and attributes the allegations to commercial or geopolitical rivalries. The company emphasizes the substantial computational cost and data-refinement work underlying its proprietary technology, noting that even its own employees do not have full knowledge of the training data.
This case underscores the importance of balancing AI technology development with protecting intellectual property rights. While AI is advancing rapidly, the discourse around potential IP infringement is still at an early stage. The DeepSeek incident highlights the complex legal and ethical challenges associated with AI development, revealing the need to reconcile technology sharing with IP protection for the sustainable growth of the AI field.
Increased Competition in AI Technology and the Importance of a Patent Strategy
As AI technology advances at a rapid pace and competition in the AI services market intensifies, AI technology patents are emerging as vital assets for achieving a competitive edge. The rise of generative AI has underscored the importance of examining patents related to AI services. With the growing availability of low-cost, high-performance AI models like DeepSeek reducing development expenses, the race to secure AI patents for technological leadership will only become more competitive.
Notably, even major tech companies such as OpenAI—which once maintained more open policies—are increasingly determined to protect their innovations by pursuing patents. This reflects the broader reality of escalating competition in AI and the heightened importance of safeguarding technological advances. The recent allegations of OpenAI’s technology being misappropriated by DeepSeek illustrate this trend vividly.
For those conducting business in the AI sphere, it is no longer sufficient to analyze patents from service-oriented providers alone (e.g., Palantir). It has become essential to examine the patents of foundational model developers like OpenAI and Google. While AI historically thrived on open-source contributions and archival data, the advent of business models with viable returns—and the corresponding drop in development costs—have shifted the landscape. Companies must now not only invest aggressively in AI technology but also shield their intellectual property through robust patent strategies to stay competitive.
AI innovations have the power to create groundbreaking services and drive societal progress, but the safeguarding of intellectual property and the ethical development of AI cannot be overlooked. To succeed in the increasingly fierce AI market and foster ongoing innovation, businesses need both solid technological foundations and well-considered patent strategies.
#PatentLaw #OpenAI #DeepSeek #MoE #AIModels #TechDisruption #ArtificialIntelligence #AIInnovation #TechTrends #MachineLearning #DeepLearning #AIResearch #AICompetition #IntellectualProperty #AIPatents #AIRegulations #TechProtection #AITransformation #AIEconomy #GenerativeAI #AIFuture #AIIndustry #BusinessStrategy #StartupGrowth #Innovation #DigitalTransformation #FutureOfTech