Crypto Exchange Dilemma: Internal Market Makers or Transparency?

In the world of crypto exchanges, the use of internal market-making teams has become a contentious issue in recent years. Internal market makers are teams of traders that work for an exchange to make a profit on the trading activity that takes place on the exchange. Some insiders argue that these teams can help contribute to the liquidity and stability of an exchange’s markets, while others believe they can create a conflict of interest that could harm investors. In this article, we’ll take a closer look at the debate over internal market makers by examining the views of two prominent figures in the crypto exchange industry – BitMEX CEO, Stephan Lutz, and Crypto.com’s internal trading teams.

BitMEX CEO’s statement on internal market makers

Stephan Lutz, CEO of BitMEX, has been a vocal opponent of the use of internal market-making teams on crypto exchanges. In an interview with The Block, Lutz argued that exchanges that make money from proprietary trading should let go of their internal market-making teams. He went on to state that there are enough high-frequency trading firms and proprietary trading shops in the market that can perform the function of proprietary trading and market-making teams, making internal teams unnecessary. Lutz’s argument is based on the idea that internal market makers can create a conflict of interest that harms investors. When an exchange’s internal market maker has access to all of the exchange’s trading information, it can use that information to its advantage, potentially at the expense of the exchange’s users. This can create a situation where the internal market maker prioritizes its profits over the interests of the exchange’s users.

Concerns have arisen over Crypto.com’s internal trading teams

Crypto.com, a popular crypto exchange, has been the subject of criticism due to its use of internal trading teams. The exchange has a team of traders who work to facilitate tight spreads and efficient markets on its platform. While the team has publicly stated that it treats its actions the same way as any other third party, many critics believe that the team’s actions could create a conflict of interest. In response to these concerns, a spokesperson from Crypto.com stated that the trading team ensures that the exchange remains risk-neutral by hedging these positions on several venues. This means that if the internal team takes a position on a particular asset, it also takes offsetting positions on other exchanges to ensure that the exchange remains risk-neutral.

Comparison with BitMEX’s past allegations of running an internal trading team

BitMEX itself faced allegations of running an internal trading team to make profits several years ago. At the time, the derivatives exchange was accused of using Arrakis Capital, an internal market maker, to trade against its own users. While BitMEX denied the allegations, it separated Arrakis Capital from the exchange to avoid the appearance of impropriety.

The use of internal market makers by crypto exchanges has become a controversial issue. While some believe that internal teams can contribute to the liquidity and stability of an exchange’s markets, others argue that they can create a conflict of interest that could harm investors. BitMEX CEO Stephan Lutz has been a vocal opponent of the use of internal teams, arguing that exchanges that make money from proprietary trading should let go of their internal market-making teams. Crypto.com has defended its use of internal trading teams, stating that its team exists to facilitate tight spreads and efficient markets on its platform. Ultimately, the decision of whether to use internal market makers or third-party firms will depend on a variety of factors, including an exchange’s priorities, its risk tolerance, and its commitment to transparency and fairness.

Explore more

Is AI Fueling Microsoft’s Record-Breaking 570 Patches?

The sheer volume of security vulnerabilities emerging within the enterprise ecosystem has reached a critical inflection point, forcing a fundamental reassessment of how major software vendors manage their codebases. As Microsoft crosses the threshold of issuing 570 distinct patches within a single reporting cycle, industry analysts are looking closely at the underlying drivers of this surge. A primary suspect in

Claude or GitHub Copilot: Which Is Best for Your Enterprise?

The current landscape of corporate technology has shifted fundamentally as generative artificial intelligence moves from being a speculative novelty to a central pillar of global production infrastructure. Today’s enterprises are no longer merely experimenting with automation or basic chatbots; they are actively integrating sophisticated “smart workers” directly into their most sensitive IT frameworks to maintain a competitive edge. This evolution

How AI Revolutionizes Social Media Analytics in 2026

The rapid integration of generative models into social media infrastructure has fundamentally altered how organizations interpret the chaotic flow of digital information. No longer are marketing professionals forced to manually sift through endless spreadsheets or rely on delayed monthly reports to understand consumer sentiment. Instead, the current technological environment provides a seamless stream of real-time intelligence that identifies shifts in

The Structural Shift Toward Creator Equity in B2B Marketing

The era of the transactional influencer campaign has reached a decisive turning point as sophisticated organizations begin to realize that renting an audience for a few weeks is far less effective than owning a share of the attention economy through permanent equity partnerships. For years, the standard operating procedure for Business-to-Business marketing involved paying flat fees for sponsored posts or

SMBs Must Adopt AI Defense to Match Rapid Cyber Threats

The sophisticated landscape of digital warfare has reached a point where manual intervention is no longer a viable primary defense mechanism for small and medium-sized enterprises. Cybercriminals are currently leveraging advanced automation and generative models to execute reconnaissance that used to take months in a matter of mere hours or even minutes. This shift in the threat actor’s playbook allows