DeepSeek's Open-Source AI Model Sparks Debate on Future of AI Development

Open-source AI is definitely happening -- the only question is how

If a powerful technology poses significant risks to business and society, should it ever be freely available? Many argue that AI falls into this category. Some even warn of existential threats. Since the advent of foundation models like ChatGPT, debates among AI experts, executives and regulators have centered around whether these models should be open-sourced. But this has been the wrong focus all along. The emergence of DeepSeek, and its creators' decision to open-source an AI model almost on par with frontier models (for significantly cheaper), shifts the debate. The question is no longer "if" but "how" we can open-source AI -- maximizing benefits while managing safety and misuse concerns. Open-source AI takes the idea beyond just code to include data, algorithms and model weights -- the learned parameters from training AI. A fully open-source AI system includes open datasets, open-source code and open model weights, but many organizations only release the model weights, which limits the ability to fully understand or rebuild the system. This becomes more complicated if the weights are trained on data that is not disclosed, potentially raising liability concerns. While openness can encourage innovation, it can also bring up questions about responsibility and security risks. But the "unexpected" rise of DeepSeek could indicate that we may be on a one-way path for AI foundation models. The shift toward openness of these models, which can fuel applications very broadly and create financial value that can further support model improvements, may prove simply inevitable. Just like Linux became the foundation for much of the software we use today, open-source AI foundation models could soon become the standard for generative AI. One of the strongest arguments for open-source AI is collective innovation. Publicly available models allow for global collaboration, accelerating breakthroughs beyond what any single team could achieve. Open models also can enhance transparency, allowing third parties to detect safety and security vulnerabilities -- though the sheer scale of modern models makes this challenging. Economic accessibility is another factor. Open AI lowers barriers to entry, enabling smaller firms, academia and governments -- especially outside Silicon Valley -- to compete. If managed correctly, this could help level the global economic playing field. But openness can also amplify risks. Unrestricted access to AI models increases the potential for misuse, such as for new forms of cyberattacks. Unlike proprietary AI, which allows model providers to better manage usage, open-source models can be modified and repurposed freely, making misuse harder to track. On the business side, open-source AI also threatens competitive advantages of model developers. Companies investing billions in training proprietary models may see their innovations commoditized, weakening funding and research incentives. Intellectual property concerns also arise: If an open model is later found to be trained on copyrighted material, who is responsible? From a technical standpoint, quality control and accountability can become major concerns. Without centralized oversight, ensuring reliable AI is difficult, although risks can be mitigated at the application level. AI relies on three key resources: data, computing power and mathematics. While debates focus on the first two, mathematical breakthroughs are the true driving force for much of the progress. Afterall, math has powered revolutions, from ancient engineering to modern AI. Progress in math, the algorithm, inevitably improves performance and efficiency. At the same time, in a competitive AI landscape, companies may strategically open-source models to disrupt rivals and gain an edge. Therefore, restricting model access may ultimately be a futile endeavor. The real surprise with DeepSeek then is that people were surprised by this. If open AI is inevitable, governance must take priority. A responsible framework should focus on three key priorities: safety technologies, governance mechanisms and international policy alignment. The rise of open AI necessitates better guardrail technologies -- integrated safety features and real-time monitoring. Just as human-generated content is moderated online, AI-generated content must be managed responsibly. Investment in tools ensuring responsible AI use is essential. AI governance should draw from best practices in internet security and software regulation. For example, model cards and data documentation can enhance transparency by detailing training data, intended use cases and limitations. Like SSL certificates for web security, a standardized AI safety certification could provide accountability without stifling innovation. Businesses adopting such standards may gain consumer trust while preempting regulatory scrutiny. Finally, AI's global implications demand some -- even if minimal -- global regulatory coordination. Europe's AI Act offers one example for responsible AI development, but it must balance innovation with risks, as well as differences across regions. Countries outside the U.S. and China can now more easily embrace AI to bridge technological gaps, making international cooperation even more pressing. Open-source AI, if governed effectively, could drive unprecedented value: accelerating innovation, enhancing global competition, and ensuring transparency and even safety. Interestingly, wider AI adoption may also provide the resources to continue the improvement of AI models outside closed walls, such as those of OpenAI. The challenge is to balance openness with risk management. By prioritizing governance, safety technologies and some international alignment, we can better ensure AI's open future is as transformative as its advocates promise. Meanwhile, the AI race is shifting from developing frontier models to applying them in the real world. DeepSeek may have marked a turning point, redirecting attention from who builds the most advanced models to how they are used. Whether businesses and regulators are ready for this shift remains to be seen, but one thing is certain: the AI landscape is changing, and the era of open AI is here to stay.

How DeepSeek and Open Source models are shaking up AI

Tech companies and academics have long wrestled with the risks and rewards of building open source software. But the frenzy around generative artificial intelligence has lent new significance to the debate. The rise of the Chinese AI startup DeepSeek has only added attention to the issue. DeepSeek recently released an open source model that it said rivaled software from the top American AI developers -- and it claimed to have done so for a fraction of the development cost, using less powerful hardware. In general, open source refers to software with source code that is made freely available for anyone to use or modify. According to the Open Source Initiative (OSI), a California-based nonprofit that advocates for the benefits of this way of working, to qualify as open source, software must comply with specific terms for distribution and access. For an AI model specifically to be considered truly open, the OSI says its developers must provide detailed information about its training data and give people the ability to study, use and modify the system for any purpose. Closed source, by contrast, refers to software and models that are controlled by the developer, can't be modified, and offer less transparency about their technical underpinnings. Do any top AI developers offer open source software? Many tech companies brand their AI software as open source; not everyone agrees they all live up to that definition. Meta Platforms Inc., French startup Mistral and now DeepSeek have all released AI models that they call open source. (OpenAI, despite having "open" in its name, does not open source most of its models.) But often such models are actually what are known as open weight models. That means that in addition to offering up the model, and perhaps some of its source code, the companies disclose the weights -- that is, the many numerical values the model picked up and was tweaked on during its training process, which allows developers to better customize it -- but not details about the data actually used to train it. Meta, for example, offers weights and some of the source code for its Llama series of AI models, but does not provide detailed information about its training data. Meta has also previously been called out by the OSI for licensing terms that include certain restrictions on commercial uses. Similarly, in January, DeepSeek said it released its latest system, R1, as an open source model, but it did not offer code or training data. That led to questions about what, exactly, the company may have used to build its model. What are the benefits of open source? Those who promote open source software typically tout it as being more affordable for users because it doesn't have the same licensing fees. Cheaper prices pave the way for broader AI adoption, and reduced development costs promote innovation. Supporters also note that the approach boosts accountability for developers creating powerful AI systems by giving others the ability to better understand how the models work. Closed systems pose the risk of producing an AI market dominated by a handful of powerful companies. Aaron Levie, chief executive officer of cloud storage company Box Inc., recently said: "In the world of very expensive and proprietary AI, the providers of AI could and likely should choose to keep all the economics for themselves -- basically crowding out opportunity for developers and the ecosystem." For companies such as Meta, there's an added benefit to open source: popularity. By allowing other developers to freely access and build on top of its open source software, Meta has been able to expand its influence throughout the AI ecosystem. What are the risks of open systems? Critics of open source software argue that it's less secure. In the case of AI, some in the U.S. fear that using such models from geopolitical rivals such as China pose a risk to national security, threatening citizens' safety -- such as by collecting massive amounts of user data that could be used for surveillance purposes. And there are concerns that American companies offering their AI models with varying degrees of openness may potentially give rival countries an opportunity to use them to one-up U.S. technological dominance. Why did DeepSeek go the "open" route? By embracing a more open approach (with some caveats), DeepSeek may have eased some concerns among global users about China's tight control of the technology. The startup also likely broadened the reach of its chatbot in Western markets by making it easier for other developers to adapt the underlying technology to meet their needs. In other words, DeepSeek effectively followed the same playbook Meta has used to capture more of the AI ecosystem -- a fact that seems not to have gone unnoticed by Meta CEO Mark Zuckerberg. "This is a huge geopolitical competition, and China's running at it super hard," Zuckerberg said in an interview on The Joe Rogan Experience. "If there should be an open source model that everyone uses, we should want it to be an American model." So how does DeepSeek's model work? Like some of the latest models from top U.S. developers -- including OpenAI and Google -- DeepSeek's R1 is intended to parrot the ways humans sometimes ruminate over problems by spending time computing an answer before responding to user queries. DeepSeek's version, which is built atop one of the company's other recently released models, V3, differs from its US peers in its efficiency, however. The team behind it worked innovatively. While rivals have used a huge number of high-powered computer chips to build similar AI models, DeepSeek team members appear to have found ways to efficiently use the relatively small amount of less-advanced ones they had access to given US export controls on the most cutting-edge chips. And they leaned heavily on a technique known as reinforcement learning that rewards a system for correct answers and punishes it for those that are incorrect. In the U.S., some tech and policy leaders have acknowledged these advances while also raising questions about whether the Chinese company built its chatbot on the back of Western technology, sidestepping some of the enormous costs of developing large language models, the building blocks of chatbots. OpenAI said it is reviewing whether DeepSeek "may have inappropriately distilled our models" to build its own rival software. DeepSeek has not responded to comments on the allegation. Back up. What is distillation? Distillation refers to using the outputs of a company's AI to train a different model -- typically a smaller, less powerful one -- to have similar capabilities. Some companies, such as OpenAI, say it violates their terms of use to use the outputs of their AI models to train a competing model. What have government officials said about open source? After conducting a review, in 2024 the administration of former President Joe Biden concluded that it would be premature to impose restrictions on open AI models but also left open the possibility that there may be reason to do so in the future. The administration of President Donald Trump has yet to fully clarify its policy on artificial intelligence, but some close to the president -- including Elon Musk and Vice President JD Vance -- have previously expressed strong support for open source AI software. In the wake of the commotion over DeepSeek, however, White House AI czar David Sacks suggested that private sector developers might make an effort to protect their models from being distilled.

DeepSeek solidified open-source AI as a serious contender -- AI founder

The release of DeepSeek, which was reportedly trained at a fraction of the cost of leading models, has solidified open-source AI as a serious challenge to centrally managed projects, Dr. Ala Shaabana -- co-founder of the OpenTensor Foundation -- told Cointelegraph. According to the founder, DeepSeek calls into question the entire paradigm of centrally managed AI, which costs tens of billions of dollars to develop and train. The OpenTensor Foundation co-founder added: "DeepSeek built a really high-performance model with a more open and collaborative approach. It proves that things like efficiency, collective intelligence, and innovation can actually rival sheer financial power." Dr. Shaabana attributed the rapid progress of open-source AI, and the narrowing of the gap between centralized systems, to a procedural shift in academia, requiring researchers to include their code with their papers in order to submit to academic journals for publication. The doctor added that increased regulations on centralized systems, including potential geographic restrictions on data due to geopolitical tensions, could place further burdens on centralized AI projects. The costs and regulatory burdens resulting from increased regulations would widen the gap between centralized and open-source systems, which are not susceptible to those constraints. Related: Crypto ETP weekly inflows fall to $527M amid DeepSeek panic and Trump tariffs DeepSeek sews panic among US politicians DeepSeek is an open-source AI model developed in China that took the AI world by surprise, causing a stock market crash, and a knee-jerk reaction from United States lawmakers. Following the disruptive launch of DeepSeek, which many characterized as a black-swan event, reports emerged that the Trump administration was considering tighter export controls on Nvidia AI chip sales to China. US Senator Josh Hawley introduced a bill to prohibit the import of AI products developed in China and the export of US-developed AI tech to China on Jan. 29. "Every dollar and gig of data that flows into Chinese AI are dollars and data that will ultimately be used against the United States," the Senator wrote in the announcement accompanying the bill. The Senator's office specifically named DeepSeek in the announcement justifying the rationale behind the introduction of the bill.