Chinese tech giant ByteDance has signed an agreement to sell a majority stake in its video platform TikTok to a group of U.S. investors. President Donald Trump announced a preliminary agreement for the sale on Sept. 19, 2025, following his negotiation with Chinese leader Xi Jinping.
TikTok CEO Shou Zi Chew told employees in a memo obtained by news organizations that the company is working to close the deal by Jan. 22, 2026. Chinese and U.S. authorities will also need to approve the deal.
The deal creates a new U.S.-only version of the app, bringing it into compliance with a law signed by President Joe Biden on April 23, 2024, and upheld by the Supreme Court on Jan. 17, 2025. Specifics of the deal remain to be hammered out, but some details are emerging. These include what will happen to the video-sharing app’s core algorithm—and what that means for TikTok’s millions of U.S. users.
The Chinese government has indicated it will not permit ByteDance to sell the algorithm, because it is classified as a controlled technology export, per Chinese law. Meanwhile, U.S. tech industry executives and some lawmakers say compliance with the law requires the algorithm to be under American control. The deal as proposed includes licensing the algorithm so that it remains Chinese intellectual property while the U.S. version of the app continues to use the technology.
TikTok’s “For You” page algorithm is widely considered the most important part of the app. As one analyst put it: “Buying TikTok without the algorithm would be like buying a Ferrari without the engine.”
The algorithm’s value lies in its uncanny capacity to anticipate users’ content preferences. Many users claim it knows them better than they know themselves—a sentiment that has evolved into a curious mix of spiritual belief and conspiracy theorizing, as my colleagues and I have documented. Other scholars have similarly noted that users feel more intimately seen and known by TikTok’s algorithm than those powering other popular platforms.
I have studied social media algorithms for nearly a decade, exploring how our relationships with them have evolved as they become increasingly entwined with daily life. As both a social media scholar and TikTok devotee, I want to shed some light on how the algorithm works and how the app is likely to change in the wake of its sale.
How the TikTok algorithm works
In some ways, the TikTok algorithm does not differ significantly from other social media algorithms. At their core, algorithms are merely a series of steps used to accomplish a specific goal. They perform mathematical computations to optimize output in service of that goal.
There are two layers to the TikTok algorithm. First, there is the abstract layer that defines the outcome developers wish to accomplish. An internal document shared with The New York Times specified that TikTok’s algorithm optimizes for four goals: “user value,” “long-term user value,” “creator value” and “platform value.”
But how do you turn these goals into math? What does an abstract concept like “user value” even mean? It’s not practical to ask users whether they value their experience every time they visit the site. Instead, TikTok relies on proxy signals that translate abstract outcomes into quantifiable measures—specifically, likes, comments, shares, follows, time spent on a given video, and other user-behavior data. These signals then become part of an equation to predict two key concrete outcomes: “retention,” or the likelihood that a user will return to the site, and “time spent” on the app.
The TikTok For You page algorithm relies on machine learning for predicting retention and time spent. Machine learning is a computational process in which an algorithm learns patterns in a dataset, with little or no human guidance, to produce the best equation to predict an outcome. Through learning patterns, the algorithm determines how much individual data signals matter for coming up with a precise prediction.
A Wall Street Journal investigation found that the amount of time users spend watching each video plays a large role in how the algorithm chooses videos it suggests to users. Using the equation it has generated to predict retention and time spent, the algorithm assigns a score to each video and ranks possible videos that could be shown to the user by this score. The higher the score for an individual user, the more likely the video will appear in their feed.
Of course, content characteristics and other users additionally inform recommendations, and there are other subprocesses folded into the equation. This step is where algorithmic moderation usually comes in. If a video looks like engagement bait or has excessive gore, for example, the content’s score will be penalized.
Here are the basics of how TikTok’s algorithm works.
What’s likely to change for US users
The sale has not been finalized, but the algorithm’s fate is coming into focus. According to reports, the United States-only version of the algorithm will be retrained on only U.S. users’ data. Users won’t need to download a new version of the app for the changed algorithm to work.
Even though the algorithm itself is the same as before, it’s fairly certain that TikTok will change. I see two key reasons for change.
First, the proposed app’s U.S.-only user population will alter the makeup of the underlying dataset informing algorithmic recommendations on an ongoing basis. As the kinds of content come to reflect American cultural preferences, values and behaviors, the algorithm may be slightly different as it “learns” new patterns.
Though users are more likely to stick with the app because they don’t need to download a new version, not all users will choose to, especially if it is seen as under the control of Trump’s allies. Under the current deal, Oracle Corp. and the U.S. government would oversee the algorithm’s retraining. This arrangement suggests that the U.S. government may have significant influence over how the app works.
The deal would give an 80% share to U.S. investors, including 50% to new investors Oracle, Silver Lake and Andreessen Horowitz. These investors have connections to Trump, and an apparent provision of the deal allows the U.S. government to select one board member.
These influences raise the possibility of a boycott from left-leaning users and creators similar to earlier boycotts of Target for rolling back DEI measures and Disney after the since-reversed suspension of Jimmy Kimmel. This may result in a user population—and data—reflective of a narrower realm of interests and ideologies.
Second, it’s possible that the majority shareowners of the new app will decide to adjust the algorithm, particularly when it comes to content moderation. The new owners may wish to modify TikTok’s Community Guidelines according to their view of acceptable and unacceptable speech.
For example, TikTok’s current Community Guidelines prohibit misinformation and work with independent fact-checkers to assess the accuracy of content. While Meta used to follow a similar approach for Instagram and Facebook, in January 2025 Meta announced that it would end its relationships with independent fact-checkers and loosen content restrictions. YouTube has similarly relaxed its content moderation this year.
With reports that the U.S. government would oversee retraining the algorithm, there’s a possibility that not only the new investors but also the government itself could influence how content is prioritized and moderated.
The bottom line is algorithms are highly sensitive to context. They reflect the interests, values, and worldviews of the people who build them, the preferences and behaviors of people whose data informs their models, and the legal and economic contexts they operate within.
This means that while it’s difficult to predict exactly what a U.S.-only TikTok will be like, it’s safe to assume it will not be a perfect mirror image of the current app.
This story was updated on Dec. 19, 2025, to include new details about TikTok’s sale.
Kelley Cotter is an assistant professor of information sciences and technology at Penn State.
This article is republished from The Conversation under a Creative Commons license. Read the original article.