Gouveia v. Investments (2026)

Case details
Full caption
Elilton Alves Gouveia v. Meridian Financial Investments
Country
United States
Jurisdiction
Florida (FL)
Court
Florida Supreme Court
Decided
2026
Majority
May (J.), joined by Conner (J.), Lott (J.)
Concurrence
Lott (J.)
DISTRICT COURT OF APPEAL OF THE STATE OF FLORIDA FOURTH DISTRICT ELILTON ALVES GOUVEIA, Appellant, v. MERIDIAN FINANCIAL INVESTMENTS, LLC, Appellee. No. 4D2025-0843 [March 25, 2026] Appeal from the Circuit Court for the Fifteenth Judicial Circuit, Palm Beach County; Maxine Cheesman, Judge; L.T. Case No. 502020CA004951XXXXMB. Elilton Alves Gouveia, Lexington, South Carolina, pro se. Cory Zadanosky of Schwed Kahle & Kress, P.A., Palm Beach Gardens, for appellee. MAY, J. The defendant in a contract dispute appeals a trial court order enforcing a settlement agreement. The defendant argues the trial court misapplied the settlement agreement’s terms in enforcing the agreement. We disagree and affirm. We write to call attention to the defendant’s apparent use of artificial intelligence in his briefing to this court. The underlying case began with a contract dispute between two companies. The plaintiff (appellee here) filed suit. The parties reached a settlement agreement, which led to the trial court dismissing the case with prejudice, but retaining jurisdiction to enforce the terms of the settlement agreement. Part of the settlement agreement provided for a forensic accounting/audit of the defendant’s company. If that audit had “any findings,” the defendant was required to pay the plaintiff up to $400,000. The audit led to yet another dispute, causing the plaintiff to file a motion to enforce the settlement agreement.
2 The trial court granted the amended motion to enforce the settlement agreement. From this order the defendant appeals. We have de novo review of an order interpreting a settlement agreement. See Sakowitz v. Waterside Townhomes Cmty. Ass’n, Inc., 338 So. 3d 26, 28 (Fla. 3d DCA 2022). The defendant argues the trial court misinterpreted the settlement agreement’s terms when it enforced the settlement agreement. The plaintiff responds the trial court correctly interpreted the agreement’s terms. We agree with the plaintiff and affirm without elaborating on the details of this appeal. • AI Spotted There once was a litigant pro se, Who let an AI lead the way. It briefed every claim, Cited cases—by name, That vanished by morning’s next day. Limerick on Pro Se Parties Using Artificial Intelligence (on file with the Fourth District Court of Appeal) (generated by ChatGPT 5.2). It appears to us the defendant used a large language model (LLM)1 to write his briefs. Popular LLMs include OpenAI’s ChatGPT, Google’s Gemini, and Microsoft’s Copilot. See In re Kenney, 2025-0389 (La. App. 5 Cir. 10/23/25), 422 So. 3d 905, 912 n.5. Technology, specifically artificial intelligence, is a marvel of the age we live in. It is an important and productive tool, but left unchecked for accuracy and legitimacy, it can be a plague upon the judicial system, creating more problems than it solves, and resulting in violation of the rules of appellate procedure. As Judge Forst reminded us: An attempt to persuade a court or oppose an adversary by relying on fake opinions is an abuse of the adversary system [….] Many harms flow from the submission of fake opinions [….] These include wasting the opposing party’s time 1 LLMs “are [artificial intelligence] systems that aim to model language, sometimes using millions or billions of parameters[.]” See LAURIE HARRIS, CONG. RSCH. SERV., IF12426, GENERATIVE ARTIFICIAL INTELLIGENCE: OVERVIEW, ISSUES, AND CONSIDERATIONS FOR CONGRESS (2025).
3 and money in exposing the deception, taking the court’s time from other important endeavors, and potential harm to the reputation of judges and courts whose names are falsely invoked as authors of the bogus opinions and to the reputation of a party attributed with fictional conduct. Goya v. Hayashida, 418 So. 3d 652, 655-56 (Fla. 4th DCA 2025) (citation modified).2 The defendant’s briefs were replete with case citations that either do not exist or fail to support the defendant’s arguments. Here, for example, the defendant cites non-existent cases such as Dausch v. Crane, 448 So. 2d 613 (Fla. 4th DCA 1984). And the defendant’s citation to Bennett v. NationsBank, 759 So. 2d 1215 (Fla. 5th DCA 2000) leads the reader to Summers ex rel. Dawson v. St. Andrew’s Episcopal Sch., Inc., 759 So. 2d 1203, 1206 (Miss. 2000), a case discussing punitive damages in a tort action, far removed from the contract issues involved in this case. Other cases cited in both the initial and reply briefs exist but address unrelated issues. See, e.g., Gross v. Lyons, 763 So. 2d 276, 277 (Fla. 2000) (adopting into Florida law the indivisible injury rule to be applied when a jury cannot apportion injury); Cohen v. Kravit Est. Buyers, Inc., 843 So. 2d 989 (Fla. 4th DCA 2003) (reversing summary judgment due to genuine issues of material fact in existence); Mullins v. Kennelly, 847 So. 2d 1151 (Fla. 5th DCA 2003) (finding 57.105 fees unwarranted); Murphy v. Bay Colony Prop. Owners Ass’n, 12 So. 3d 924 (Fla. 4th DCA 2009) (finding error in the trial court’s dismissal of a case based on the merits); De Groot v. Sheffield, 95 So. 2d 912, 916 (Fla. 1957) (discussing the quasi-judicial proceedings of the Civil Service Board); and Broward Cnty. v. G.B.V. Int’l, Ltd., 787 So. 2d 838, 845 (Fla. 2001) (discussing the writ of certiorari and site plans/plat applications). By this opinion, we put the defendant on notice that future unchecked use of artificial intelligence in filings with this court may result in sanctions for failure to comply with Florida Rule of Appellate Procedure 9.210(c). Affirmed. 2 Separately, the Florida Third District Court of Appeal ordered a pro se appellant to show cause why he should not be sanctioned for using fake and semi-fake citations in his briefs. See Takefman v. Pickleball Club, LLC, 418 So. 3d 826, 827 (Fla. 3d DCA 2025).
4 CONNER and LOTT, JJ., concur. LOTT, J., concurs separately with opinion. LOTT, J., concurring. I concur fully in the majority’s well-written opinion. I write separately to highlight the need for prophylactic, rather than remedial, solutions to the problem of improper use by pro se litigants of AI chatbots. That is not to minimize the well-recognized problems of improper use of AI by attorneys, particularly where AI generates hallucinated or fake authority that the attorney submits to the court without verification. But courts have been properly and adequately responding to this problem by using existing rules and tools to sanction attorneys who engage in this improper conduct. That toolbox works well enough for attorneys. Attorneys are repeat players in litigation. Sanction them, and they will learn from it. Monetary sanctions imposed on attorneys, who tend to be solvent, can make their adversaries whole for the time wasted by misconduct. If they repeatedly disregard sanctions orders, more severe discipline can be imposed by courts or state bars. Over time, I have no doubt that courts’ consistent response will lessen the problem of improper AI use by attorneys. Pro se litigants, on the other hand, are usually not repeat players in the court system. The case at hand is their case. They have little experience or knowledge on how to litigate cases and how to, or not to, use tools like generative AI in that litigation. All this creates a problem for the courts, for at least three reasons. First, remedial sanctions or warnings, like the one the Court rightly imposes on Appellant today, do nothing to prevent the problem of the continued use of AI by new pro se litigants who never received such warnings. Second, there is a seemingly endless deluge of AI-generated drivel submitted by pro se litigants who have never received such warnings. I will not bother to collect authority sanctioning it; it is ample. Even more of it is dealt with in unpublished orders. Most commonly, the recalcitrant litigant simply loses without court comment on the AI problem, which is often the most economical way for a court to dispose of a given dispute. Any judge on any bench right now understands the pervasiveness of the
5 problem. Third, the AI-generated slop that pro se litigants serve up is a unique sort of gruel. Unlike real lawyers, AI Chatbots, at least in their current form, do not “think.” They make predictions about what words ought to come next in response to a prompt that the user provides it.3 This technology is very good at sounding right, but less adept at being right, especially where critical thought is required in creation of the content. Pro se litigants, reasonably, often do not appreciate the distinction and, lacking legal training, do not appreciate how or why a response might not be right. But it sounds right, so they put it in their brief to see what happens. And since the cost to generate the content is so low, they can put in a lot of it. The opposing party and the court are left in the position of breaking down why something that sounds right is not right, which tends to consume more resources than parsing through a traditional pro se appeal. Pro se litigants of course cannot be faulted for using these tools. The lack of affordable legal services has been a perennial problem in the courts and legal professions. The problem is that AI Chatbots appear to the untrained eye to be a solution. But unless cautiously and thoughtfully wielded, they are no solution; they make the problem worse. So in order to meaningfully solve the AI-slop problem, we need to get pro se litigants to understand, up front, that blind reliance on a Chatbot for legal assistance is not acceptable. This is a much more difficult task than warning or sanctioning litigants on the back end. I have not seen a perfect solution. Some courts have implemented rules or standing orders requiring all litigants, attorney and self-represented alike, to disclose the use of AI and 3 E.g., Snell v. United Specialty Ins. Co., 102 F.4th 1208, 1227 n.7 (11th Cir. 2024) (Newsom, J., concurring) (“As I understand things, the LLM that underlies a user interface like ChatGPT creates, in effect, a complex statistical ‘map’ of how people use language—that, as machine-learning folks would say, is the model’s ‘objective function.’ How does it do it? Well, to dumb it way down, drawing on its seemingly bottomless reservoir of linguistic data, the model learns what words are most likely to appear where, and which ones are most likely to precede or follow others—and by doing so, it can make probabilistic, predictive judgments about ordinary meaning and usage.”).
6 certify its accuracy.4 This is probably the right starting point, and I would support adoption of such a requirement for this Court. Chatbots are going to get better, and that’s going to make these problems worse. The question now must be how to address them on the front end. * * * Not final until disposition of timely-filed motion for rehearing. 4 E.g., Jim Ash, 11th and 17th Circuits Order Disclosure, Certification of AI Use in Court Filings, The Florida Bar (Feb. 9, 2026) (online at https://www.floridabar.org/the-florida-bar-news/11th-and-17th-circuits-order-disclosure-certification-of-ai-use-in-court-filings/)
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