After failed delays, Apple files formal request with Supreme Court to review Epic case


The Supreme Court could now weigh in on the Apple versus Epic case where Apple was found in contempt of an injunction and forced to allow all developers to link externally without commission.

The Apple versus Epic saga is nowhere near an end even if Epic is celebrating a victory prematurely. Even as the case returns to Circuit Courts, Apple is requesting the Supreme Court to review two specific issues it has with the proceedings so far.

In the Supreme Court filing viewed by AppleInsider, Apple shares that the scope of the anti-steering injunction exceeds the District Court’s limits set by CASA. It also argues that the injunction violation was issued in error due to suggesting it was violating the “spirit” of the law rather than the letter.

Its arguments in the 34-page filing suggest that the Supreme Court should take up these matters because Apple’s is a perfect vehicle to address these issues. Apple asserts that providing a decision would settle matters for future cases, and if left untouched, could cause the CASA verdict to be a dead letter.

Basically, Apple hopes that there are enough discrepancies to ensure the Supreme Court at least picks up the case. In the meantime, Apple will continue its proceedings with Epic in the lower courts.

The story so far

Epic sued Apple in 2020 on antitrust grounds, but Epic lost on every count except one. That count pertained to Apple’s anti-steering practices.

Large blue-tinted screen showing a stylized apple wearing sunglasses and speaking to rows of shadowy seated viewers, evoking a dystopian surveillance or propaganda broadcast atmosphere

Epic’s ‘1984’ parody ad

Apple removed the anti-steering provisions and provided a new, if complex, way for developers to link to external purchases. It meant developers still owed Apple a commission, 12% or 27%, even if it directed customers to the web.

Even though Epic filed the case and it wasn’t a class action, the injunction was applied to all developers based in the United States. Apple clearly planned to appeal that point even then, but then things were made more complicated.

Epic filed a complaint, which resulted in Apple being found in contempt. However, the original injunction didn’t mention anything about Apple’s commission, and the violation was argued in spirit.

Various appeals and arguments later, and Apple has been told it is owed a commission, even on external links. The problem is, Apple would have to come back to court and decide on the commission rate with Epic.

That’s how the case has arrived at the Supreme Court. And even though Apple tried to get the proceedings halted in the lower courts, twice, it must now face both at once.

Apple’s arguments

The foundations of Apple’s arguments appear to be sound. The courts do appear to be ignoring the precedent set by CASA.

Three Fortnite character silhouettes dancing: a bulky bear-like figure on green, a muscular fighter on pink, and a slim figure with glowing eyes on purple, all in playful poses

Epic’s iPod-like ad

The Supreme Court ruled that lower courts were exceeding their jurisdiction by applying injunctions outside the scope of a case. However, the 9th Circuit has argued that there is an antitrust exception to CASA that would allow the decision in Apple’s case to stand.

Apple believes very strongly that this effectively bypasses the Supreme Court’s ruling and authority. That’s why it said it would render the CASA case a dead letter.

The other argument also has to do with how the 9th Circuit does business. Apple argues that in the other Circuit Courts, civil contempt is applied only if the letter of the law is violated, not the spirit.

Even if you don’t care about any of this legal back and forth, it is still incredible that the Epic Games lawsuit has reached this point. It started with a “1984” parody ad starring an apple wearing sunglasses and could finish with setting incredibly important precedent via the Supreme Court.

If Apple wins the “in spirit” portion of its arguments, Apple gets to carry on with its previous 12% and 27% commission rates for external linking. It would also mean proceedings in the lower courts would return to appeals stages.

If the universal nature of the injunction is thrown out, then only Epic will be affected by Apple’s move away from anti-steering practices. It would mean a total and abject failure of a case that cost Epic over a billion dollars already.

Apple has requested that its petition be considered during the Supreme Court’s June 25 conference. Perhaps Epic’s CEO should hold off on celebrations until after that date.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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