How More Accessible AI Could Revolutionize Wall Street Trading | Global News Avenue

Wall Street’s largest high-frequency trading (HFT) company has long relied on expensive proprietary trading systems to gain its competitive advantage. But a new challenger may emerge from an unexpected direction: Open Source Artificial Intelligence (AI). While traditional financial powerhouses invest millions of dollars in proprietary algorithms, platforms such as Chinese AI startups DeepSeek Free trading technology or almost free trading technology can be provided to anyone. This shift raises an important question: Can cheaper and more accessible AI reshape Wall Street, or can traditional infrastructure and expertise barriers maintain the status quo?

Harry Mamaskidirector of financial research at Columbia Business School and an expert in using AI in finance, noted that DeepSeek is the culmination of many developments. “A lot of AI has Open source,” he told Investopedia, referring to camelan AI model launched by Meta (Yuan), the company embraces the face.

“The tricky part is getting the hardware running, feeding the data to it, and then customizing the general model to a specific use case,” Mamaysky said.

Below, we will introduce you to how to use open source AI in finance.

The evolution of artificial intelligence transactions

Wall Street deals have been dominated by elite companies that have proprietary AI systems – cheap algorithms have developed a lot of resources behind closed doors. These institutions have historically maintained their strengths by combining deep pockets, professionals and complex computing infrastructure. Recent industry analysis shows that developing advanced AI transaction models requires investments ranging from $500,000 to over $1 million before taking into account ongoing talent retention and infrastructure maintenance costs.

The date of using AI in transactions dates back to the 1980s, when companies first started to use simple rules-based systems to conduct automated transactions. The real transformation was in the late 1990s and early 2000s machine learning algorithm Improve the quantitative trading strategy of the times. Major companies like Renaissance Technology and De Shaw are pioneering the market model using sophisticated AI models and execute transactions at an unprecedented rate. By the 2010s, High-frequency trading (HFT) Powered by AI has become the cornerstone of market operations, with the largest company investing hundreds of millions of dollars in computing infrastructure and talent to maintain its competitive advantage.

DeepSeek and similar open source AI initiatives are challenging this traditional model with their collaborative development approach. Instead of locking the algorithm, these platforms leverage the collective expertise of the global developer community that constantly improves and improves technology.

However, joining is not as easy as downloading open source code. Although these new tools narrow down certain barriers to entry, they do not automatically upgrade the competition environment. The traditional trading system is deeply embedded in market operations and has been tested for years. The challenge of open source alternatives is not only to match the complex capabilities of established systems, but also to prove that they can be executed reliably within the demanding limitations of real-time transactions.

In addition, companies that employ open source AI systems still have to develop the right operating frameworks to ensure regulatory compliance and establish the infrastructure needed to effectively deploy these tools. So while open source AI may reduce the cost of complex trading technologies, it is unlikely that you will soon download an open source AI trading platform like an open source notes application application.

Cost and accessibility

One of the most attractive aspects of open source AI is its potential to reduce upfront costs. Traditional proprietary systems require significant investment in licensing fees and custom software. Continuing partnership between Citadel LLC and Alphabet Inc.GOOGL), for example, leveraging more than one million virtual processors to reduce complex computing time from hours to seconds, but it requires a lot of ongoing infrastructure investment.

DeepSeek’s open source approach provides a sharp contrast. Its V3 and R1 models are free and it uses a MIT license, which means it can be modified and used for commercial purposes. Mamaysky notes that while the software may be free, implementing it effectively requires the following significant investments,

• Computing infrastructure and hardware
• High-quality market data acquisition
• Safety measures and compliance systems
• Ongoing maintenance and updates
• Expertise for deployment and optimization

While you can certainly access DeepSeek’s latest models and download code for free, the need to successfully deploy it in an HFT environment is much more than that.

Transparency and accountability

The advantage of the often touted open source AI is its inherent transparency. With publicly reviewed source code, stakeholders can review algorithms, verify their decision-making processes, and modify them to comply with regulations or meet specific needs. A good example is the International Commercial Machinery Company (IBM) AI Fairness 360, a set of open source tools for auditing and mitigating bias in AI models. Additionally, with publicly available architectural details and training data for Meta Llama 3 and 3.1 models, developers can evaluate compliance with copyright and other regulatory and ethical standards. This level of openness is withBlack boxThe nature of a proprietary system, internal operations are hidden, sometimes leading to opaque decisions that may not be unfolded even by the creators of the system.

However, describing all proprietary trading systems as opaque black boxes would be misleading. Major financial institutions have made great strides in improving the transparency of their AI models, both depending on regulatory pressures (such as the EU AI Act and the Evolving U.S. Guidelines) and internal risk management needs. The key difference is that while proprietary systems build their transparency tools internally, the open source model benefits from community-driven auditing and verification, often speeding up the problem of solving problems.

Innovation gap

DeepSeek’s R1 model breakthrough has attracted the attention of industry leaders – even Openai’s Sam Altman admitted in early 2025 that the “historically wrong side” about the open source model, suggesting a potential shift in the way the industry develops collaboratively.

Nevertheless, Mamaysky says the real challenge of achieving the commitment to the transition to open source AI lies in three key areas: scaling the hardware infrastructure, ensuring high-quality financial data, and adapting a common model for specific transaction applications. Therefore, he believes that the advantage of a good company will not disappear anytime soon. “In my opinion, open source AI itself poses no risk to competitors. The revenue model is data center, data, training and process robustness,” he said.

Geopolitics makes AI race more complicated. Former Google CEO Eric Schmidt warned that the United States and Europe need to focus more on building open source AI models or risk losing ground to China in this regard. This shows that the future of financial AI depends not only on technical capabilities, but also on broader strategic decisions about how transaction technology develops and shares.

Bottom line

The emergence of open source AI platforms like DeepSeek may represent a major shift in financial technology, but they have not yet threatened established orders on Wall Street. While these tools will significantly reduce software licensing costs and increase transparency, Mamaysky warns that for these companies, “whether manufacturing models may not be a first-order issue”.

What we may see is a hybrid future with open source and proprietary systems. So the question is not whether open source AI will replace traditional Wall Street systems, but how to integrate it into them.