January 10, 2024

Will Marra on Bloomberg Law Blog: Litigation, Professional Perspective- AI & the Future of Litigation Funding

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William Marra

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January 10, 2024

Artificial intelligence (AI) has arrived at the US Supreme Court—kind of. Though the term “artificial intelligence” has not yet appeared in the U.S. Reports, Chief Justice John Roberts’ year-end report on the federal judiciary talks about how AI will impact the law.

Chief Justice Roberts writes that “[m]achines cannot fully replace key actors in court,” especially the role of judges. Trial judges must assess credibility and make fairness determinations; appellate judges may determine not only what existing law says today, but how it should “develop in new areas.” “[H]uman judges,” Chief Justice Roberts predicts, “will be around for a while.”

On the other hand, Chief Justice Roberts writes, “[p]roponents of AI tout its potential to increase access to justice, particularly for litigants with limited resources” and “those who cannot afford a lawyer.”

AI is not the only recent advancement that addresses the extraordinarily high cost of legal services. Our legal system is also being transformed by litigation funding and litigation insurance, which allow litigants to offset the mounting costs of litigation by sharing risk with third-party funders and insurers. Indeed, at least before AI burst onto the scene, litigation funding was called “likely the most important development in civil justice of our time.” Bloomberg Law recently listed litigation funding as one of the six most important legal issues to watch in the litigation space in 2024, alongside hot button issues like abortion and gender identity.

How, then, does the revolution of AI interact with the transformative impact of litigation funding? It’s too early to say for sure, but here are four suggestions as we head into 2024.

Better, Quicker Funding Decisions

The first impact of AI and funding is likely that the technology will dramatically increase the efficiency with which litigation funders operate.

One common criticism of litigation funding is that the diligence process is too slow. AI should accelerate funders’ time to decision, as they bring to bear machine-learning tools to more quickly analyze cases. Speeding up the diligence process will make the funding application process more efficient and client-friendly.

AI tools should also increase the accuracy with which funding determinations are made, as funders—and the lawyers submitting matters for funding—harness the power of AI to better predict dispute outcomes. This should in the long run decrease the cost of litigation funding, since machine learning input will decrease the risk that funders are taking.

Decreased Demand for Funding?

Litigation funding began as a response to an endemic problem: litigation is really, really expensive.

AI promises to alleviate that problem by reducing the cost of litigation—even if lawyers still need to cite check AI’s suggestions. In some instances, then, AI is likely to reduce or eliminate the need for litigation funding, particularly for those litigants who are liquidity-constrained and lack resources to fund today’s multi-million-dollar litigations.

The truly impecunious, or the increasing number of well-capitalized litigants who use funding for risk management, are still likely to use litigation funding. But the total amount they require for funding may decrease, as the cost of litigations goes down.

Overlooked Cases May Be Funded

On the other hand, AI may enable funders to finance matters that are currently overlooked by most litigation funders.

For example, many funders do not finance smaller cases that require only a few hundred thousand dollars to litigate. The cost of reviewing those matters is simply too high relative to the possible return. As the cost of studying matters decreases, however, funders may find these opportunities to be attractive.

Meanwhile, there is also a thin market for especially expensive cases, particularly high-risk and high-cost matters like sprawling antitrust disputes. As the cost of legal services declines, so too might the cost of litigating those cases. The strongest of these cases might get increased access to litigation funding.

AI may further assist litigation funders in better identifying claims that ought to be brought and can benefit from funding. This can include either the identification of filed cases that are good candidates for funding, or the identification through publicly available documents of latent claims that should be filed.

Beyond Plaintiff-Side Litigation

Tracking all this is yet a third revolution in the law: relaxed restrictions on law firm ownership by third parties. In 2020, Arizona eliminated state legal ethics Rule 5.4, the rule that bans non-lawyer ownership, in an effort to “promote business innovation in providing legal services at affordable prices.” Utah has a regulatory sandbox with relaxed restrictions, and other states may follow suit.

Litigation funding typically focuses on just that: litigation—and usually plaintiff-side litigation to boot. The elimination of bans on non-lawyer ownership of law firms will allow third-party capital and AI to help reduce the cost of legal services not only in litigation but also in other areas, including defense litigation, corporate work, trusts and estates, and so on. This will be a very good thing for people and companies who need greater access to those legal services.

This article was originally published by BloombergLaw.com

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In 2025, OpenAI acquired io, the hardware venture founded by former Apple design chief Jony Ive and a group of other Apple alumni, for a reported $6.5 billion, and set out to build its first consumer hardware device, widely expected to compete directly with the iPhone.² To staff that effort, OpenAI hired aggressively from Apple. According to the complaint, more than 400 former Apple employees now work at OpenAI.³ Two of those hires anchor Apple’s allegations. Tang Yew Tan spent roughly 24 years at Apple, where he served as a vice president of product design responsible for the iPhone and Apple Watch, before becoming OpenAI’s chief hardware officer. Chang Liu spent about eight years at Apple as a senior systems electrical engineer before departing for OpenAI in 2026.⁴ Apple’s theory is not that a single rogue employee walked out the door with a file. It is that the movement of talent was accompanied by a coordinated effort, one Apple describes as operating “at every level," to extract and exploit the confidential information those employees carried in their heads and on their devices.⁵ The Allegations The complaint reads less like a garden-variety departure dispute and more like a catalog of the exact conduct the Trade Secret Guide warns companies to watch for. Among Apple’s central allegations: Apple claims OpenAI’s hardware leadership directed recruiters to use Apple’s confidential project code names during the hiring process, and instructed job candidates to bring “actual parts” and “CAD/design artifacts” to their interviews.⁶ It alleges that OpenAI circulated internal Apple documents marked “Need to Know” that coached departing employees on how to evade Apple’s exit-security procedures, including the “dreaded walkout,” and to alert OpenAI before signing their exit agreements.⁷ The specifics attributed to individual employees are what give the complaint its texture. Apple alleges that Chang Liu exploited an authentication bug to reach internal network storage after his access should have been cut off, messaging a colleague, “LOL, I found out I can access the [network storage], so funny,” and noting within hours of his departure that he “still ha[d] another computer.”⁸ And Apple alleges that io “exploited and used Apple’s secret, proprietary industrial design techniques,” misleading one of Apple’s own manufacturing partners about whether it was authorized to use a confidential metal-finishing technique.⁹ The trade secrets Apple says are at risk span the full arc of its product-development process: technical specifications for unreleased technologies, engineering presentations and prototype data, component and vendor selection processes, and the proprietary manufacturing techniques that turn a design into a shippable product.¹⁰ Notably, Apple’s opening ask is not a damages windfall. It is protection. Apple seeks to bar OpenAI from using or disclosing the information at issue, to compel the return of its confidential materials, and to preserve the evidence.¹¹ In other words, Apple is using the courthouse to do what its NDAs and exit interviews were supposed to do: keep its edge inside the building. OpenAI’s Response OpenAI has pushed back hard, and its answer is a preview of the fault lines any trade secret plaintiff should expect to fight over. On August 6, 2026, OpenAI moved to dismiss, characterizing the alleged conduct as “benign, lawful conduct” that Apple has mischaracterized, and arguing that its hardware executives simply followed standard industry recruiting practices.¹² As to Chang Liu, OpenAI contends he was “trying to help Apple” by assisting former colleagues who asked him to locate work information, not stealing anything.¹³ More pointed, and more instructive, is OpenAI’s argument that Apple’s own conduct undermines its case. 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The value of a trade secret program is only as good as the “reasonable measures” behind it; OpenAI’s opening move is to argue that Apple’s own iCloud and offboarding practices were not reasonable at all. The ability to describe what was taken, with specificity, is not a formality. It is frequently the whole ballgame at the pleading stage. And the human element — recruiting, exit procedures, the “dreaded walkout” — is where secrets actually leak, long before anyone reaches a courtroom. Companies that treat these as compliance checkboxes learn the hard way, in a complaint, that they were the strategy all along. For those of us who evaluate disputes for a living, Apple v. OpenAI is also a reminder of why high-stakes trade secret matters are among the most compelling on the plaintiff’s side. The conduct is often concrete and documentable, the competitive stakes are enormous, and, as the Federal Circuit’s recent decision in Versata Software v. Ford underscored, the damages framework can reach the full value of what the misappropriation delivered to the wrongdoer, not merely a discounted license fee. That combination is exactly what makes these cases worth pursuing, and worth backing. Apple’s complaint will be tested, as it should be, and the allegations remain just that — allegations. But the strategic signal is already unmistakable. When the most valuable company in the world wants to defend its future, it reaches for trade secret law. Certum Group’s Trade Secret Guide is built to help plaintiffs and their counsel do the same, whatever their size, and this case is a live illustration of why that playbook matters now more than ever. Certum Group can help. If you are evaluating a trade secret dispute or want to talk through options for funding or de-risking one, get in touch . Footnotes ¹ Complaint, Apple Inc. v. OpenAI, Inc. , No. 5:26-cv-07078 (N.D. Cal. filed July 10, 2026); see Apple sues OpenAI over alleged trade secret theft , TechCrunch (July 10, 2026). ² The wildest allegations in Apple's trade secrets lawsuit against OpenAI , TechCrunch (July 13, 2026). ³ Id. ⁴ Apple sues OpenAI over alleged trade secret theft , TechCrunch (July 10, 2026). ⁵ Apple sues OpenAI alleging trade secret theft, says scheme was "at every level," CNBC (July 10, 2026). ⁶ The wildest allegations in Apple's trade secrets lawsuit against OpenAI , TechCrunch (July 13, 2026). ⁷ Id. ⁸ Id. ⁹ Id. ¹⁰ Apple sues OpenAI over alleged trade secret theft , TechCrunch (July 10, 2026). ¹¹ Id. ¹² OpenAI Asks Judge to Toss Apple's Trade Secrets Lawsuit , Claims Journal (Aug. 7, 2026). ¹³ Id. ¹⁴ OpenAI says Apple's own security practices undermine its trade secrets case , TechCrunch (Aug. 6, 2026). ¹⁵ Id. ¹⁶ OpenAI Asks Judge to Toss Apple's Trade Secrets Lawsuit , Claims Journal (Aug. 7, 2026).
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