Key takeaways:

• • • Stability is the story 鈥� The OBBB’s main value has been predictability for business planning, not sweeping new rules 鈥� a sharp contrast to the disruption of prior major tax legislation like the TCJA.

    • Section 1202 is a live opportunity 鈥� The expanded QSBS exclusion has reopened planning conversations around corporate structuring that had cooled in recent years.

    • Plan for both today and tomorrow 鈥� Practitioners should help clients capitalize on current certainty while preserving flexibility, and they should help clients build tax positions that can hold up to increasingly AI-powered IRS scrutiny.

When major tax legislation lands, the instinct is to brace for upheaval. But one year after the passage of the (OBBB) Act, the consensus among practitioners is notably different: The OBBB didn鈥檛 rewrite the rules so much as confirm them, and that distinction has mattered more than it might sound.

Certainty over seismic change

Unlike the (TCJA) 鈥� which was passed in 2017, mostly took effect the following year, and forced practitioners to relearn much of the code 鈥� the OBBB’s significance lies less in what it changed and more in what it settled. It gave businesses a stable set of rules to plan against, rather than a moving target to which to react.

“From a purely tax lens, it was… easier to unpack than in prior years because there are fewer seismic changes,鈥� says , Partner at Plante Moran, reflecting on the past year under the OBBB. 鈥淚t was providing a lot of clarity that just [meant], at least for the next several years, we had the rules.”

That clarity is not a small thing. Multi-year business decisions 鈥� around such big-ticket items as entity structuring, capital investment, and succession planning 鈥� depend on practitioners being able to tell clients that the rules will hold. Thus, OBBB’s real contribution was buying back that predictability.

Section 1202 comes back to life

If one provision captures the OBBB’s practical impact, it’s the revitalization of 鈥� the qualified small business stock (QSBS) gain exclusion. The expansion of this program has done more than simply adjust a technical detail; indeed, it has reopened a whole category of planning conversations that had gone quiet.

“The action around the qualified small business stock gain exclusion… has really reinvigorated the Section 1202 planning conversations,鈥� Eckert explains. 鈥淯ltimately, what we got was an expansion of the program. So, what that has done is reinvigorated those conversations around planning into corporate structures.”

For founders, investors, and the tax advisors who serve them, that means is back on the table 鈥� and often earlier in a company’s lifecycle than before, since the incentive to structure correctly from the outset is now more valuable.

A new kind of advisory opportunity

Of course, stability doesn’t mean passivity. If anything, the OBBB has expanded what tax professionals can offer clients. With a known set of rules, advisors can move beyond compliance and into genuine strategy by helping clients maximize their position under current law while still preparing for the fact that today’s certainty has a shelf life.

That balance 鈥� seize the moment, but don’t get comfortable 鈥� is a concept that isn鈥檛 lost on many tax specialists. “Maximize your opportunities today but also have a long-term view while having flexibility and preserving flexibility wherever you can, and knowing and anticipating that there could be future changes,” Eckert says, framing this moment as a broader opening for the profession, not just a technical one.

Legislative clarity, he argues, gives practitioners a reason to go deeper with clients than simply processing the next filing. “From a practitioner lens, I think [legislative changes] are a huge opportunity… giving us an opportunity to really bring value to our clients and to also get to know our clients better,鈥� he notes. 鈥淚t’s been, in a certain sense, a great opportunity to just build deeper relationships.”

In other words, the firms getting the most out of this environment aren’t the ones treating the OBBB as a compliance checklist; rather, they’re the ones using it as a reason to have a better conversation with clients about where they’re headed.

The IRS isn’t standing still either

The one area in which practitioners should definitely not get comfortable is enforcement. A smaller IRS workforce doesn’t mean lighter scrutiny 鈥� it likely means a different kind. As the agency leans more heavily on AI-driven tools, its ability to examine returns at scale is set to expand even as headcount contracts.

“I think across the board, we’re certainly aware of that and are counseling clients on the need to establish and build positions and think carefully about it,鈥� Eckert explains. 鈥淚n a world of AI-enabled tools, the scrutiny may actually increase, and the ability for the IRS to quickly and efficiently examine lots of data is something that could certainly exist.”

That means that tax advisors need to help their clients build positions that can withstand more sophisticated review, not less. Meticulous documentation and defensible reasoning matter more, not less, in an environment in which fewer human examiners can still cover more ground with better tools.

One year in, the OBBB’s legacy isn’t a story of dramatic reform, but rather it’s a story of tax firms and their clients finally getting room to plan. The tax advisors making the most of that room are the ones using it to build sharper strategies and deeper client relationships, all while keeping an eye on an IRS that’s quietly getting more capable of deeper examination.

You can find more of听our coverage of the One Big Beautiful Bill Act听here

Key takeaways:

• • • The wash sale loophole is likely closing 鈥� For clients that have been harvesting crypto losses and immediately repurchasing the same asset should know that 鈥渨ash sale鈥� strategy may soon work exactly like it does for stocks 鈥� with a mandatory 30-day waiting period.

    • Non-compliant holders have a potential off-ramp 鈥� A proposed voluntary disclosure program would let clients that haven’t properly reported digital asset income to get into compliance with reduced penalties 鈥� but it鈥檚 only available for a limited time.

    • Staking and mining income treatment is changing 鈥� Proposed legislation would allow taxpayers to elect to defer recognizing newly minted digital assets as income, which could be a meaningful planning opportunity for active miners and stakers鈥� or a trap, depending on their situation.

Walk into any conversation with a cryptocurrency-owning client right now and you’re navigating the same awkward reality: The rules are genuinely unclear, have been unclear for years, and yet the IRS has increasingly expected compliance anyway. Now, however, the U.S. House Ways and Means Committee is trying to resolve that tension.

And crypto legislation is one piece of a much larger shift reshaping the tax profession and potentially impacting clients right now. The recent 2026 State of Tax Professionals Report from the 成人VR视频 Institute maps the challenges and opportunities defining the profession this year, including AI adoption, advisory pricing, talent constraints, and the growing gap between what clients want and what firms are charging for it.

Add to that list now, the changes coming for crypto asset owners and their tax, audit & accounting advisors.

New legislative changes for crypto owners

The package of crypto legislation 鈥� a collection of seven separate bills 鈥� currently under consideration by Ways and Means is serious enough that their tax advisors need to start thinking now about what it means for clients.

Some of these new proposals include:

The wash sale rule: A strategy that may be changing

Of all the provisions in the package, extending wash sale rules to digital assets will have the broadest practical impact. Currently, crypto investors can sell at a loss, immediately buy back the same position, and still claim the deduction 鈥� a strategy unavailable to stock investors. The proposed legislation would change that, applying to digital assets the same 30-day before-and-after window that governs stock transactions.

For clients with active portfolios, this isn’t just a planning consideration 鈥� it’s a recordkeeping one. Every transaction would need to be evaluated against a rolling 60-day window across potentially multiple wallets and exchanges. The change to this rule was hardly unexpected 鈥� the question was never really whether the wash sale rule would come to crypto, but when. Tax advisors should begin their honest conversation with clients by acknowledging that.

Mining and staking: A choice with consequences

For clients who mine or earn staking rewards with crypto, the proposed gives crypto miners and stakers the ability to elect to defer income recognition, which would treat newly minted digital assets more like self-created property than an immediate taxable event.

In practice, the calculus is complicated. Deferring income means the cost-based question gets pushed forward, not eliminated. If the asset appreciates significantly before sale, a client who deferred income recognition could face a larger ordinary tax event later. If the asset depreciates, owners have lost the ability to recognize the loss in the year of receipt.

Making the right choice 鈥� with the advice of a tax professional 鈥� depends almost entirely on the client’s individual circumstances, such as their marginal tax rate, their expectations for the asset’s trajectory, and their liquidity needs. This is exactly the conversation that tax professionals need to be having with clients around this issue.

The voluntary disclosure program: A limited window

Perhaps the most immediately actionable provision for many tax advisors is the proposed one-time voluntary disclosure program, which gives taxpayers who haven’t properly reported crypto income the opportunity to get into compliance with reduced penalties and a clean slate.

The IRS has run these programs before, and the pattern is consistent 鈥� the best terms are early, enforcement pressure increases after the deadline, and clients that wait because they hope the problem will disappear tend to regret it.

Simplification and opportunity

Not everything in the package adds complexity. would exclude gains or losses on network fees and regulated US dollar stablecoins by removing a reporting headache that has made crypto compliance so cumbersome for everyday users. And the Charitable Deductions for Digital Asset Donations Act would eliminate the qualified appraisal requirement for donated digital assets when market prices are readily available, lowering the friction on a strategy that has always made good tax sense for clients that holding appreciated crypto with charitable intent.

The tax advisors that will offer their clients the most value in a post-legislation world are the ones already holding these proactive conversations, and reviewing which clients have crypto exposure, identifying which may have unreported income, flagging which miners and stakers should be thinking about the deferral choice, and identifying charitable giving opportunities before the appraisal requirement disappears.

In addition, the voluntary disclosure program is the clearest example of how proactive advisory work can pay off. Clients that have quietly hoped their unreported crypto transactions would stay below the radar need someone to tell them plainly that a window for clean resolution is likely opening 鈥� and that waiting for it to close is not a strategy. That conversation is uncomfortable, of course, but it鈥檚 also exactly what a trusted advisor is for.

Beyond compliance, the considered package of crypto legislation creates the need to have genuine planning conversations that didn’t exist before. For example, the wash sale question is time-sensitive, and the staking deferral election requires modeling. None of this requires tax advisors to wait for final regulations; rather, it requires they know their clients well enough to know which ones have exposure, which have opportunity, and which needs a conversation they haven’t thought of requesting.

Right now 鈥� in the space between a Congressional hearing and a presidential signature 鈥� that is the most valuable thing a tax professional can offer.

You can download a copy of the 成人VR视频 Institute鈥檚听2026 State of Tax Professionals Report here

Key findings:

• • • Workloads grow, while staffing stays flat 鈥� Many government legal department professionals say their work keeps increasing while staffing remains stagnant; and many are turning to AI tools to improve capacity.

    • AI adoption is surging 鈥� Over the past year, AI adoption among government legal departments has spread rapidly, with federal and state agencies leading the way.

    • Unfortunately, AI oversight hasn鈥檛 surged 鈥� Many legal departments report that their AI governance is lagging behind adoption, with 20% of agencies having no AI use policy in place at all.

Government legal departments are facing an all-too-familiar problem: more work, more complexity, and the same number of staff to do the job, according to the 成人VR视频 Institute鈥檚 2026 Government Legal Department Report, which captures the insights from 200 government legal department professionals at varying levels.

Jump to 鈫�

2026 Government Legal Department Report

Threaded through these insights, some clear trends emerged. For example, technology 鈥� especially AI and other advanced tools 鈥� is increasingly serving as an extension of staff, expanding agencies鈥� capacity to manage rising workflow demands.

Increasing pressures across all levels

More than one-third of respondents report that their workload increased by more than 10% in the past year, with many handling between 21 and 50 legal matters per week. At the same time, workloads are becoming more complex, with more than one-third of respondents saying that more than half of the legal issues they face are complex, which is particularly notable at the state and federal levels.

Staffing shortages, a top concern in recent years, continue to persist. Three-quarters of respondents say their agencies experienced staffing shortages over the past two years, and almost two-thirds say they anticipate shortages into 2027.

Indeed, despite an increase in complexity and workload, attorney staffing levels have stayed the same for almost 40% of agencies, the report shows. And at the federal and state level, departments were more likely to have experienced a reduction of more than 10% of their staff.

AI adoption skyrockets, making governance more necessary than ever

More than one-quarter of respondents say their agency or department is now using AI tools, up from a meager 5% last year, with this increase taking hold at the federal and state level much more quickly. Among the different groups of respondents, one-third of federal and state government legal professionals report using AI tools compared to just 19% of those at county and city departments. Resistance to AI is diminishing, too; however, more than one-third of county and city legal departments still report having no plans to use AI.

Optimism toward AI is rising alongside implementation, the report shows. More individuals at the federal and state level feel optimistic than pessimistic about AI technology, which is an inversion of last year鈥檚 sentiment. Among county and city legal professionals, pessimism still remains more common. Among all respondents, confidential data exposure remains the top evaluation criterion when assessing these advanced tools.

The report underscores that this all points to a need for the establishment of strong governance models before adoption. Nearly two-thirds of government agencies and departments have an AI use policy in place or are developing one, respondents say. However, 1-in-5 departments and agencies are still without an AI use policy, risking unofficial use of prohibited AI tools.

Those agencies hesitant to implement AI technology are encouraged to view AI technology as a way to increase staff capacity amid flat staffing, rising workloads, and growing matter complexity. AI tools can help reduce strain on employees, contributing to better-managed workloads while reducing employee burnout. When appropriately vetted, however, AI technologies can reduce administrative burdens, increase legal research efficiency, and help those organizations facing trying to manage more work with the same staffing levels.

An actionable path forward

As the report makes clear, AI is no longer a future challenge; rather, it鈥檚 a present reality in a rising percentage of government legal departments. Indeed, the report outlines ways departments and agencies can move forward in this space, by beginning with lower-risk foundational tools like legal research and case management systems; and then investing time in developing thoughtful AI use policies and evaluation protocols. With responsible staff training and a thoughtful evaluation process, AI technologies can protect the valuable time and work-life balance of government legal professionals.

Increasing workloads are not optional for government legal departments, but how department leaders empower their staff to manage these workloads is becoming the differentiator.

You can download

a full copy of the 成人VR视频 Institute’s “2026 Government Legal Department Report” by filling out the form below:

Key insights:

• • • Judges can lead by example in responsible AI adoption 鈥� By engaging thoughtfully with AI and using it to enhance (and not replace) human judgment, judges can set an important standard for accountability, verification, and ethical use across the legal system.

    • AI excels at streamlining preparation, not decision-making 鈥� AI is most valuable for reducing the burden of preparatory tasks, such as summarizing briefs, organizing records, identifying questions, and clarifying technical or statutory language. It should not substitute for judicial reasoning, discretion, or decision-making.

    • Verification and guardrails are non-negotiable 鈥� Responsible AI use requires vetted tools, strong privacy protections, precise prompting, and independent verification of all AI-generated content. The integrity of judicial work depends on maintaining these disciplined processes.

There is a leadership opportunity sitting inside every courtroom in America, and most judges haven’t claimed it yet.

AI is already shaping the legal system, in how attorneys research, how clerks draft, and how litigants prepare their cases. In , the latest webinar from the听听鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI) 鈥斕鼵hief Justice Matthew Fader of Maryland observed that the 鈥渢echnology is just becoming ubiquitous.鈥�

鈥淚t’s spreading faster than any technology I’ve encountered in my lifetime,鈥� Chief Justice Fader said. The reality, of course, is that AI will influence a wide spectrum of legal processes, including how judicial work gets done. The question is whether judges will be passive observers of that shift, or active, thoughtful participants in it.

The advantages in engaging with AI extend beyond efficiency 鈥� it is also about leadership. Judges who use AI responsibly, with clear guardrails, careful verification, and a firm commitment to human judgment, can model the standard that the legal profession needs from the bench.

The preparation problem AI can actually solve

The bulk of the work for a successful hearing or trial is done long before the judge enters the courtroom. This happens through careful review of records, briefs, and complex subject matter. Yet time constraints often limit thorough preparation, and this is where AI adds real value.

When used appropriately, AI can streamline early-stage work without compromising judicial rigor. Judges can upload briefs and receive structured summaries of each party’s arguments, providing a clear roadmap before deep analysis begins. Transcripts can be searched instantly for specific testimony, and lengthy records can be organized by topic, which reduces manual review.

For more technical cases, AI can generate concise primers on unfamiliar subjects, helping judges engage expert testimony within the proper context. AI also can draft targeted oral argument questions, flag hallucinated or incorrect citations in briefs, identify factual inconsistencies, and create procedural checklists for motions or default judgments. It can even simplify complex statutory language to enhance the clarity of proposed jury instructions.

As Justice Linda Kevins of New York observed, the more creatively AI is applied, the more use cases emerge. Judges who begin with one small task often discover a range of new efficiencies, which transforms preparation from a burden to a strategic advantage.

Responsible use is the point

What separates a judge using AI as a thoughtful professional from one using it carelessly is the discipline built around it. And that starts before the first prompt.

Any tool a judge uses should be vetted by the court’s IT department, its administrative office, or both. The terms of service matter, such as whether prompts are retained, whether data is used for model training, whether confidential information could be exposed. These are not technical details to delegate, rather they are the minimum a judge should understand before uploading anything.

Prompting with intention matters too. Instructing AI tools to always cite its sources, to flag uncertainty, and to present arguments neutrally are habits that will produce better output and reduce the risk of erroneous data. Asking for authority behind every claim, and explicitly telling the tool not to guess, should become standard practice. The right questions to ask before any task, as Dr. Maura Grossman of the University of Waterloo, described it, are: “What are you trying to accomplish? And what’s the best tool for that?”

And verification is non-negotiable. AI tools can confuse a dissent with a majority holding, misstate what a lower court decided, or misquote language.

One simple rule, offered by Justice Kevin summed it up: “Whatever it gives me, I have to verify.” Every case that AI identifies, every citation it provides, every factual summary it generates must be checked against primary sources.

This is not a burden that undermines the value of AI; indeed, it is the practice that makes AI valuable.

What AI cannot do

In AI use in courts, the boundaries matters as much as the capability of the tools.

“This is not a truth-finding tool,鈥� explained Chief Justice Fader. 鈥淭his is a truth-agnostic, predictive tool.” AI generates statistically likely output based on patterns in its training data. It does not reason, weigh competing values, or sense that something in a case doesn’t add up. It has no judgment, no empathy, no moral compass, and no capacity to recognize when the law should evolve because justice requires it.

Judicial discretion is not an inefficiency AI can optimize away 鈥� and this is the point. As Justice Kevins said in the webinar, AI is “only an assistant, an extra assistant.” The analysis of how the law applies to a specific set of facts, before specific parties, in a specific moment 鈥� that is unavoidably human work. The moment any judge asks AI to assess which party has the stronger argument, a vital task has been delegated to the AI that should not have been.

Leading from the bench

Judges who engage with AI carefully set a visible standard for everyone who appears before them. These judges are better equipped to recognize when a filing鈥檚 AI-generated content is inaccurate. They can set informed expectations for clerks and staff, and they can ask the right questions when AI-related issues surface in litigation 鈥� many of which are already surfacing regularly.

The goal for judges is to use AI 鈥渢o do their work more productively, but not to replace their judgment,鈥� said Chief Justice Fader. And that balance should guide responsible judicial use: Neither avoidance, which is increasingly unrealistic; nor uncritical adoption, which carries real risk 鈥� but instead a more disciplined approach than either of these extremes.

The prepared judge is not simply the one who has read everything before walking into a courtroom. It’s the one who has used every available tool wisely, verified rigorously, and exercised the human judgment that no algorithm can replicate. That standard, consistently applied, is a form of leadership for which the legal profession has been waiting.

For more on the impact of AI in courts, visit the听

Key insights:

• • • The history of discovery is a history of matching tools to tasks 鈥� From paper review to keyword search to machine learning to generative AI, each innovation succeeded by assigning the right level of capability to the right kind of problem.

    • Adoption has outpaced understanding 鈥� AI discovery tools are now widely used, but the statistical literacy needed to defend their use in court has not kept pace.

    • Courts reward preparation, not adoption 鈥� Judges generally don’t weigh in on which tool a law firm uses; rather, they weigh in when counsel cannot explain or defend that choice.

Document review has always been organized around a basic question: Who, or what, is best suited to handle a given volume and type of material? In the paper era, that question was answered with people. Large cases were staffed with tiers of reviewers, working through boxes of documents. The model was labor-intensive but logical. More documents required more reviewers, and the work was distributed according to experience level, with junior reviewers handling first-pass relevance calls and more experienced attorneys handling privilege determinations and quality control.

The widespread shift to electronic discovery in the early 2000s introduced keyword search as an intermediate tool. Search terms allowed reviewers to narrow enormous document populations before human eyes even glanced at them, but the approach had limits as well. A search for “ice cream”, for example, would miss a document that said “gelato,” and a poorly constructed term list could either bury reviewers in false positives or miss documents entirely.

The next major shift came with the introduction of supervised machine learning in the late 2000s and early 2010s. This was a different kind of tool entirely. Rather than searching for fixed terms, the system learned from examples. A reviewer would mark documents as relevant or not relevant, and the system would apply that pattern across the rest of the collection, prioritizing documents most likely to matter. This was the first point at which legal discovery began to resemble a partnership between human judgment and statistical inference, rather than a purely manual or purely mechanical process.

Bringing GenAI in

The most recent shift, beginning around 2022, introduced large language models and generative AI (GenAI) into the same workflow. These tools do not simply sort or rank documents 鈥� they can summarize them, answer questions about them in natural language, and construct chronologies or timelines across a collection.

Each of these innovations did not in fact replace the one before it so much as they added a new layer of capability. Indeed, the central challenge in every era has been the same: The important determination is knowing which tool, or which level of human or machine capability, is appropriate for a given task.

For more on this, check out , featuring Dr. Maura Grossman

This is where the analogy to legal staffing becomes useful. A first-year associate and a senior attorney are not interchangeable, because they are suited to different kinds of judgment calls. The same logic increasingly applies to AI tools. A general-purpose AI model is not the same as a fiduciary-grade system built and validated for a specific legal task. Choosing a narrowly designed, purpose-built tool over a general one is the AI equivalent of choosing an senior attorney over a law school graduate for a task that requires accountability and demonstrated reliability, not just general competence.

The literacy gap that follows adoption

As these tools have become more capable, the legal profession’s ability to evaluate them has not necessarily kept pace. Evaluating AI discovery tools, both before they are adopted and after they generate output, requires such as recall, precision, confidence intervals, and margin of error. In fact, these are not concepts most lawyers were trained to work with, and many rely heavily on vendors to supply and interpret these metrics rather than developing the capacity to do so themselves. The risk is a quiet one: figures or citations that should raise concern can go unexamined simply because the people reviewing them do not know what to look for.

This gap matters because it shapes how discovery disputes are litigated. If one party argues that another’s recall rate is too low 鈥� meaning that a significant share of relevant documents was not produced 鈥� both sides need a working understanding of how that figure was calculated and what it means in order to argue the point credibly. The legal and technical questions in such disputes cannot be fully separated.

What courts actually expect

Discovery is , with judges generally staying out of methodology decisions entirely. Judicial involvement in AI-driven discovery is therefore a signal, not a routine occurrence. In fact, courts usually step in under two circumstances: i) a dispute over whether a chosen method is adequate; or ii) a timing problem severe enough to threaten the case schedule. Both situations typically indicate that something has already broken down. A low recall rate that prompts a motion or a delay serious enough to draw judicial questions, for example, suggests a deeper flaw in how the review was designed or executed and is sometimes serious enough that it can call an entire production into question rather than just the documents in dispute.

The common thread in these failures is governance, not technology. A capable AI tool used without a clear validation process, defined oversight roles, or documented standards for measuring output is no more reliable than an undertrained reviewer left unsupervised. Most of the disputes that escalate to a judge can often be traced back to a preventable gap: whether no one verified the tool’s metrics, no one understood what the numbers meant, or no one assigned responsibility for catching errors before production.

Those law firms that build proper AI governance into their discovery process, have clear protocols, defined accountability, and have a working understanding of how their tools are validated, are far less likely to need a judge to resolve what should have been caught internally.

The history of document review, at its core, is a history of matching capability to task, whether that capability comes from a person or a machine. As AI tools take on more of the discovery process, the skill that increasingly distinguishes effective use from risky exposure is not technical operation, but the ability to select the right tool for the task, understand what its outputs mean, and recognize those instances in which human review remains essential.

To learn more about how courts should approach AI and other advanced technology, check out the 成人VR视频 Institute’s

Key insights:

• • • Sect. 301 and IEEPA tariffs operate on fundamentally different legal foundations 鈥� The IEEPA tariffs flow from an executive emergency declaration that can be unwound overnight, while Sect. 301 findings are built on a formal evidentiary record that can survive numerous administrations.

    • Those manufacturers that diversified away from China now face compounded exposure 鈥� The countries to which many manufactured moved their trade operations 鈥� including Vietnam, India, Bangladesh, and Malaysia 鈥� are now named in the recent Sect. 301 action.

    • Managing this complexity without purpose-built tools is no longer realistic 鈥� The need for access to quality vendor data, tariff classifications, country-of-origin mapping, and duty layering requires systems that can be updated continuously, not spreadsheets that are reviewed quarterly.

Since early 2025, manufacturers have lived in a tariff environment defined by volatility that鈥檚 been dictated seemingly at the whim of the United States. Rates announced one week were paused the next, country-specific deals emerged from diplomatic calls, and 90-day exemptions became the operating rhythm. For supply chain teams, the rational response was to treat every new tariff as provisional 鈥� something to monitor, not necessarily something to plan around.

That logic does not apply to the of the U.S. Trade Representative (USTR), under听Section 301 of the Trade Act of 1974听that a list of 60 economies 鈥� comprising the largest US trading partners 鈥� had failed to enforce a ban on goods produced with forced labor听are therefore were听restrictive to US trade.

To understand why, it actually requires and how it compares to the International Emergency Economic Powers Act (IEEPA), which the Trump Administration had used as its authority behind the 2025 reciprocal tariffs until that was disallowed .

Unlike the IEEPA, Sect. 301 is not an executive power that turns on or off depending on when a national emergency is declared. Rather, it is a statutory framework that requires the USTR to conduct a formal investigation, gather evidence, hold public hearings, and build a record before making an actionability determination. In the June 2 action alone, the USTR received testimony from nearly 60 witnesses and almost 500 public comments before issuing its findings.

That record matters, because it is what makes tariffs issued in response to Sect. 301 findings structurally resistant to reversal. Unwinding them requires either a new formal determination, a negotiated bilateral resolution in which the trading partner actually changes its practices, or Congressional action. A new administration cannot simply issue a presidential order lifting them because the legal bar is categorically higher.

And this distinction is no longer theoretical. After the Supreme Court ruled his tariffs invalid, President Trump immediately pivoting to Section 122 of the Trade Act of 1974, which permits a temporary global surcharge of up to 15% for no more than 150 days. That took effect February 24, and is set to expire July 24, unless extended by Congress. Tariffs imposed because of the June 2 Sect. 301 findings were never exposed to the same legal vulnerability and is now the administration’s primary vehicle for building durable tariff authority.

There is also a political dimension that compounds the durability. The June 2 findings are grounded specifically the failure of the named economies to prohibit the importation of goods made with forced labor. That framing carries broad bipartisan support in Washington, and neither party is positioned to argue against forced labor prohibitions, which means the political incentive to reverse these tariffs is far weaker than it was for the IEEPA-based tariffs.

The compounded exposure problem

For manufacturers that spent 2024 and 2025 diversifying their supply chains away from the tariff-heavy China, the June 2 findings create a specific and uncomfortable problem. The most common destinations for that diversification 鈥� Vietnam, Bangladesh, India, Malaysia, Thailand, and Indonesia 鈥� are all named in USTR’s recent action. Proposed additional duties of 10% to 12.5% would layer on top of existing duties and any Sect. 122 tariffs still in place during the transition period.

In other words, the move that looked like risk mitigation then may now carry its own tariff exposure now 鈥� and unlike the situation in 2025, there is no obvious alternative jurisdiction.

That means vendor management systems that integrate tariff data in real time 鈥� pulling current duty rates by code, flagging country-of-origin changes, modeling landed cost across multiple sourcing scenarios 鈥� are no longer a competitive advantage. Now they are a baseline operational requirement. The same applies to supplier compliance documentation. As forced labor attestations become relevant to exclusion eligibility under Sect. 301, having those records organized, current, and accessible is not an audit-readiness question, rather, it鈥檚 a cost-of-goods question.

Then, the practical challenge for manufacturers becomes an operational one, not just a strategic one. Tracking tariff exposure across dozens of suppliers, multiple countries of origin, layered duty structures, and evolving classification rules is not a task that can be easily scaled with traditional tools. For example, in the 24 hours following the Supreme Court鈥檚 tariff ruling, the US terminated one tariff regime, enacted a replacement under a different statute, and announced the launch of multiple new Sect. 301 investigations. A manufacturer鈥檚 spreadsheet that鈥檚 updated monthly cannot keep pace with a regulatory environment moving at that speed.

The durable lesson

The IEEPA tariff experience trained supply chain teams to stay nimble 鈥� and then demonstrated exactly how fragile executive-action tariffs can be when the Supreme Court invalidated them. That instinct toward flexibility still has value, of course; however, the Sect. 301 framework requires a parallel capability that requires manufacturers to recognize when a tariff is structural, model its long-term cost impact, and adapt sourcing and vendor strategies accordingly.

These new Sect. 301-based tariffs are not a negotiating position waiting to be resolved. They are a legal determination, built on a formal record, grounded in a cause 鈥� the elimination of forced labor from global supply chains 鈥� that has strong consensus across the political spectrum.

Those manufacturers that plan around them as permanent while investing in the tools to manage that complexity in real time will be better positioned than those waiting for the next exemption announcement.

You can find out more about how tariffs continue to impact global trade here

Key insights:

• • • Unauthorized practice of law doctrine is a poor fit for regulating AI and legal technology 鈥� UPL remains important when people represent others in court or affect legal proceedings, but it does not translate cleanly to software. Extending UPL to software moves the doctrine away from its original purpose and toward regulating the existence of tools rather than the risk of harm.

    • Legal technology should be regulated through consumer protection principles, not tool-based restrictions 鈥� Rather than asking whether an AI product or software platform “practices law,” regulators should focus on whether the tool misleads users, performs as advertised, provides adequate transparency, or causes demonstrable consumer harm.

    • A harm-based framework can protect consumers while expanding access to justice 鈥� Because most legal needs remain unmet, AI and justice tech can provide meaningful support to people who would otherwise receive no legal help. Safe harbors, disclosures, and accountability for fraud, negligence, or false advertising would better balance innovation with consumer protection.

This is the second of a two-part blog series examining how regulators, the legal profession, and individual litigants are looking at the unauthorized practice of law (UPL). We first looked at the history of UPL, and now this installment suggests a consumer protection-based method of regulation to replace today鈥檚 supplier-based regulations.

The legal industry has reached an inflection point in which the unauthorized practice of law (UPL) doctrine is no longer the right framework for regulating modern legal services. As technology reshapes how legal help is delivered, the focus must shift from policing who can provide legal support to ensuring that consumers are protected from harm.

This does not mean dismantling the doctrine entirely. UPL continues to serve an important function in which individuals represent others in court or otherwise engage in conduct that affects the integrity of legal proceedings 鈥� because courts need enforceable boundaries around who may appear before them.

Those considerations, however, do not translate cleanly to today鈥檚 AI-driven advanced technology. Extending UPL to software may shift the doctrine away from its original purpose and toward regulating the existence of tools rather than the risk of harm. A more effective approach is to apply existing consumer protection principles 鈥� such as fraud, negligence, and false advertising 鈥� to evaluate whether legal technologies are delivering accurate, transparent, and reliable support for users.

UPL continues to serve an important function in which individuals represent others in court or otherwise engage in conduct that affects the integrity of legal proceedings 鈥� because courts need enforceable boundaries around who may appear before them.

Over the past several decades, we have bent UPL law to fit emerging models of legal assistance, but with AI we have reached the point at which we should not attempt to stretch it further. AI has expanded the capabilities of legal technology beyond document automation to include research, summarization, and guided analysis. These tools are now widely available and are being used by individuals who would otherwise navigate legal issues without assistance.

At the same time, familiar regulatory arguments are being applied to these new tools. Assertions that such systems constitute UPL rely on assumptions that are increasingly difficult to reconcile with how these technologies function and are used.

Users generally understand that these systems are not lawyers. Engagement with AI tools is driven by accessibility, cost, and convenience, particularly in a landscape in which a substantial portion of legal needs go unmet. The more relevant inquiry is whether these tools provide a meaningful improvement compared to having no assistance at all.

Shifting from UPL to consumer protection

When consumers turn to technology, outcomes will vary, as they do across all forms of legal support. When issues arise, existing legal frameworks already provide mechanisms for accountability. Questions of fraud, negligence, and misleading representations can be addressed through established consumer protection laws without relying on an expanded interpretation of UPL.

Focusing on demonstrable harm rather than the mere existence of a tool aligns regulatory efforts with their intended purpose. It also reduces the risk of limiting innovation that could address persistent gaps in access to legal services encountered by so many individuals today. When liability is tied to the mere provision of technology, rather than to harmful conduct, the result is often reduced investment and slower development of potentially beneficial solutions.

Some jurisdictions have begun to adopt approaches that reflect this distinction. Non-prosecution policies, disclosure requirements, and clearly defined safe harbors for AI-driven tools can provide a framework in which innovation can proceed alongside appropriate safeguards. Indeed, these models emphasize transparency and consumer awareness while allowing for continued experimentation and improvement.

Legal doctrine in transition

Whether state regulators drive the change or watch from the sidelines, there are broader legal considerations on the horizon. On the federal level, the U.S. Federal Trade Commission (FTC) has already signaled that a change towards harm-based regulation as opposed to tool-centered regulation is coming.

In his concurrence in , which had billed itself to consumers as 鈥渢he world鈥檚 first robot lawyer,鈥� :

鈥淢y vote should not be taken as support for the State Bar of California鈥檚 claim that DoNotPay was engaged in the unauthorized practice of law. The Commission does not enforce state occupational-licensing laws like California鈥檚 unauthorized-practice-of-law prohibition. And if a company were to create a computer system capable of giving accurate legal advice and drafting effective legal documents, or honestly advertise a system that provides something less, I doubt that the aggressive enforcement of lawyers鈥� monopoly on legal service would serve the public interest.鈥�

This aligns with remarks from U.S. Supreme Court Justice Neil Gorsuch鈥檚 confirmation hearing when he stated: 鈥淲hy is it that every time certain companies that provide online legal services for basic things get sued every time they move into a new State?鈥�

While approaching it from different angles, courts are arriving at similar positions. In , an Oregon court of appeals case about fabricated citations, the court stated: 鈥淩egardless of provider, a generative artificial intelligence program is not, itself, a lawyer.鈥� While perhaps not intended, the logical extension of this is that a tool cannot be engaged in UPL 鈥� only the human using it can.

Finally, recent judicial reasoning, such as in the Supreme Court case of , suggests a closer examination of attempts to regulate speech by categorizing it as professional activity. The Court found professional speech protected by an 8-1 vote, suggesting bipartisan questions about whether professional licensing can continue to restrict speech.

Aligning regulation with reality

Within this evolving landscape, the central policy question is how best to protect consumers while enabling meaningful improvements in access to legal support. Frameworks designed for earlier models of service delivery can be difficult to apply effectively to new forms of technology without producing unintended consequences.

Instead, a consumer-focused approach can direct attention to the quality, accuracy, and transparency of services. It can evaluate whether users are misled, whether tools perform as described, and whether harm can be identified and addressed.

While AI-driven technology offers a way to extend the reach of legal support systems and to develop solutions that operate at a scale not previously achievable, the concept of UPL is still a valid one, albeit within a more limited and clearly defined scope.

As the legal system rapidly evolves, aligning regulatory approaches with current realities allows for both the protection of consumers and the development of the tools that expand access to justice in practical and sustainable ways.

You can find more about the challenges around issues of access justice here

Key insights:

• • • Agentic AI acts autonomously, creating new accountability challenges 鈥� Agentic AI acts and makes decisions with minimal human intervention, and this shift changes everything about responsibility and oversight.

• • • With intentional design, the risks can be addressed confidently 鈥� Silent failures, accountability diffusion, and confidentiality breaches can only be mitigated through governance, testing, and rigorous human oversight.

• • • AI is changing legal work, not eliminating it 鈥� When agentic AI handles routine tasks, legal professionals can move their attention onto higher-value work and increased responsibilities.

It is no longer useful to treat all AI as a single category or a tool for a single use case. Generative AI (GenAI) has already begun reshaping legal work by drafting documents, researching precedents, and answering questions with remarkable speed. At its core, however, GenAI remains a responsive tool. Agentic AI, on the other hand, represents a distinct evolution. Rather than waiting for a prompt, agentic AI systems can plan workflows, carry them out autonomously, and make decisions along the way.

As technology and the judicial system become increasingly intertwined, it is essential to examine where these more advanced tools intersect and what that convergence means for legal institutions. Ankita Upadhyay, Senior Director of AI Enablement at 成人VR视频, recently shared her perspective during a recent webinar,听, presented by the听听鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI) 鈥斕齛nd offered valuable insight into the opportunities and responsibilities that accompany this shift.

One of the key notes to understand from the panel is that 鈥済enerative AI gives you an answer. Agentic AI takes an action 鈥� and that distinction changes everything about the accountability,” Upadhyay said, adding that the distinction is not merely technical. It must reshape how we think about professional responsibility and the integration of AI into institutions that are built on trust and accuracy.

The promise of efficiency and transformation at scale

The potential of agentic AI is already visible in courts across the country. In Palm Beach County, Fla., for example, court officials are using agentic AI to process incoming documents at unprecedented scale. When an attorney files a document, the system autonomously identifies the document type, classifies it, extracts data, and routes it appropriately. The county already has processed up to 5 million documents using this process, operating 20 hours a day, every day of the year.

The most important part isn’t just the volume, however, it’s what happened to the people.

The staff who spent time on routine document processing were not laid off; instead, they were reassigned. Clerk 1 positions were transitioned to Clerk 3 and Clerk 4 roles, and that meant greater responsibility, more complex decision-making, and increased compensation for those making that transition.

“The staff that was doing all the processing of documents has been reallocated to customer experience and more complex tasks,” explained Parik Chokski, Director of IT for Palm Beach, on the webinar. This development reflects a broader truth: AI is not taking jobs in the legal sector; rather, it鈥檚 changing what those jobs entail.

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As more repetitive work moves to AI, legal professionals move their attention toward the kind of work that demands their judgment, expertise, and accountability.

Risks are real, but not insurmountable

Yet the promise of agentic AI comes with genuine risks that differ from those posed by generative AI. Because agentic AI acts autonomously, for instance, failures can occur silently and invisibly, and sometimes repeatedly before detection.

成人VR视频鈥� Upadhyay identified three predominant risks for legal professionals and their organizations with agentic AI use:

1. Accountability diffusion 鈥� When an agentic AI system produces a document through a chain of autonomous decisions, it becomes difficult to determine where human judgment ended, and machine decision-making began. This ambiguity directly challenges professional conduct rules, which assume lawyers make every material decision. The result is an unclear line of responsibility and potential legal exposure for the lawyer.

2. Confidentiality at scale 鈥� Agentic AI systems operate across entire databases and multiple use cases simultaneously. A single misconfiguration of permissions can allow an AI agent to access privileged information to which it shouldn’t have access, potentially sharing sensitive client data across unintended matters. The danger lies in the fact that this often happens silently and repeatedly until discovered.

3. Irreversibility 鈥� Unlike GenAI, where a flawed draft often gets caught during review, agentic AI can send client communications, file documents, or update records based on faulty reasoning even before human oversight intervenes. The speed of action outpaces the speed of review, and thus, it creates a gap that traditional legal processes weren’t designed to address.

“The risk isn’t that AI gets it wrong,鈥� Upadhyay said. 鈥淭he problem is agentic AI systems, when it gets things wrong, it happens silently in a black box until you monitor it, and that’s the biggest challenge.”

Guardrails for responsible implementation

Given this, how do courts and legal organizations implement agentic AI thoughtfully? The webinar panelists, drawing on real-world implementations and NCSC research, emphasized several critical actions, including:

Establish clear governance 鈥� Begin with centralized registration of all agentic AI agents, conduct rigorous risk classification based on task impact, and start with low-risk workflows before advancing to high-stakes tasks. “Having a proper agentic AI governance is really important,” Palm Beach’s Chokski said.

Commit to rigorous testing 鈥� Extensive stress-testing in development and Q&A environments must precede any production deployment. Palm Beach’s implementation required weeks, if not months, of testing before going live 鈥� but that investment paid dividends in reliability and organizational confidence.

Design for transparency 鈥� Build workflows with built-in checks, balances, and fail-safes. Establish comprehensive logging that tracks what the AI agent does, what permissions it has, and what decisions it makes at each step. Monitor continuously for behavioral drift.

Maintain human oversight 鈥�”Trust but verify,” Chokski noted. Agentic AI is powerful and here to stay; but so are human professionals, and they must always retain oversight, the ability to intervene, and ultimate accountability for outcomes.

The conversation continues

The choice legal organizations face today is not whether agentic AI will exist, but how to engage with it responsibly.

Organizations that approach agentic AI with intentionality, clear frameworks, and commitment to human judgment will unlock its potential to expand capability, improve efficiency, and free legal professionals to do work that requires their expertise and accountability. Those that rush forward without guardrails risk silent failures that could undermine trust in both the technology and in the overall institution.

The path forward demands partnership: AI handles scale and speed, while humans provide judgment, accountability, and ethical reasoning. When those work two parts work together intentionally and with clear guardrails, that’s where justice is served.

For more on the impact of AI in courts, visit the听

Key insights:

• • • The Panama Papers transformed beneficial ownership 鈥� The release of the Papers in 2016 changed the idea of beneficial ownership from a technical compliance footnote into a global policy imperative, and the pressure has not let up.

    • Regulatory responses have been significant but uneven 鈥� The EU has pushed forward aggressively, while US reforms under the Corporate Transparency Act have been substantially narrowed.

    • For compliance professionals, the enduring lesson is not about any single regulation 鈥� Rather, compliance professionals should have one goal: Maintaining the discipline of asking who, ultimately, is behind the transaction.

When 11.5 million documents from Mossack Fonseca were published on April 3, 2016, compliance teams across financial institutions around the world faced unprecedented pressure from senior leadership to prove they actually knew the true identities of their clients’ beneficial owners. A decade later, establishing that ultimate ownership remains both the most important and the most difficult task in anti-money laundering compliance.

A watershed moment, but not a starting point

It would be a mistake to credit the Panama Papers with inventing beneficial ownership as a compliance concern. The Financial Action Task Force (FATF), an intergovernmental organization created to promote anti-money laundering (AML) activities, had long emphasized the risks of anonymous shell companies. The United Kingdom was already developing its Persons with Significant Control register; and the United States鈥� Treasury Department鈥檚 Financial Crimes Enforcement Network (FinCEN) had a draft of customer due diligence guidance in circulation before a single Mossack Fonseca document was made public.

Yet, what the leak of the Panama Papers did was something more powerful than create law 鈥� it created political will.

The leak showed, with granular specificity, how shell companies, nominee directors, layered trusts, and intermediary accounts could be stacked together to place meaningful distance between regulators and the individuals who actually control the assets. These were not fringe techniques; rather, they were routine services offered at scale to clients in more than 200 jurisdictions. The “gatekeeper problem” 鈥� the tendency of lawyers, accountants, and formation agents to introduce clients without responsibility for verifying who those clients ultimately were 鈥� was no longer theoretical. It was documented, widespread, and systemic.

What the decade of response produced

The regulatory response to the Panama Papers was substantial, even if ultimately uneven in execution.

In the US, FinCEN’s 2016 CDD Final Rule standardized what many institutions were doing selectively: requiring identification and verification of beneficial owners of legal-entity customers using a 25% ownership threshold and a control prong. For the first time, this was an enforceable expectation across covered financial institutions 鈥� not a best practice, but a mandate.

The regulatory response to the Panama Papers was substantial, even if ultimately uneven in execution.

Globally, the momentum was stronger. The European Union moved through successive Anti-Money Laundering Directives, expanding registration requirements and tightening obligations for designated non-financial businesses and professions. Ultimately, the EU established the Anti-Money Laundering Authority (AMLA) in its 2024 package to deliver cross-border supervisory consistency. And the FATF’s revised Recommendation 24 in 2022 raised the bar further, shifting the mission from collecting beneficial ownership data to ensuring it is accurate, current, and verifiable, with timely access for competent authorities. Having a register is not the same as having reliable information, and regulators have spent a decade making that distinction explicit.

The 2020 FinCEN Files added a further dimension. Where the Panama leak exposed the formation agents who were enabling shell company abuse, the FinCEN Files implicated the banks themselves, showing that suspicious activity reports (SARs) were being filed on transactions that institutions continued to process. Together, these successive leaks sustained the political will that the Panama Papers first generated.

The data is only as good as what’s behind it

The Panama Papers exposed that beneficial ownership frameworks could be gamed in ways that left regulators technically satisfied but substantively blind. Nominee arrangements created paper trails that went nowhere, and outdated register entries gave the appearance of compliance while concealing real control.

The lesson that proved most durable is that transparency requires verification, accessibility, and enforcement working together. A register without verification is a filing cabinet, verified data without accessible reporting channels is compliance theater, and accessible data without enforcement consequences for misrepresentation is an honor system.

For compliance professionals today, this translates into a concrete operational expectation. Enhanced scrutiny for complex legal entity customers is not optional. Nominee arrangements, offshore links, unexplained control structures, and identifying a politically exposed person (PEP) are not risk factors to note and move past. They are the scenarios that point to where the framework is most likely to fail, and examiners know it.

Where the picture gets complicated

Today, further progress is real, but uneven. In the US, the Corporate Transparency Act of 2021 was the most ambitious attempt to extend beneficial ownership reporting to companies themselves, not just the financial institutions serving them.

Under FinCEN’s March 2025 interim final rule, that ambition has been significantly narrowed: US-formed entities and US persons are now exempt, with reporting obligations falling primarily on certain foreign entities registered to do business domestically. That outcome followed a prolonged and contentious legal battle, involving multiple conflicting injunctions, a Supreme Court intervention, and sustained pushback from small business and industry groups, which ultimately made a political resolution rather than a judicial one the path of least resistance for the U.S. Treasury Department.

听The core problem shone by the Panama Papers leak in 2016 remains unresolved. A decade of regulatory response has only narrowed it.

Real estate reporting faces its own legal turbulence, with the Residential Real Estate Rule vacated and on appeal; and investment adviser AML coverage has been pushed to 2028, a delay driven in part by industry objections and competing agency priorities. These are not minor footnotes; rather, they are meaningful gaps in a system that was supposed to be closing.

Enforcement outcomes globally have been equally inconsistent. Panama’s own courts in a major Panama Papers-related trial in 2024. And Germany charged , the firm’s co-founder, in 2026. Jurisdiction still matters enormously, which is precisely what offshore structures were designed to exploit.

The durable lesson

Of course, none of this means the decade of reform was without consequence. It simply means the work is not done.

The Panama Papers’ most important legacy is not any specific regulation; rather it鈥檚 a permanently elevated expectation around knowing your customer, not just by name, but by ultimate beneficial owner, control structure, the credibility of information on file, and the ongoing monitoring that keeps that picture current. The most effective AML programs treat beneficial ownership as a living element of the customer relationship, not a checkbox at onboarding.

Still, the core problem shone by the Panama Papers leak in 2016 remains unresolved. A decade of regulatory response has only narrowed it and made it significantly harder to exploit, but as compliance professionals know better than most, the absence of a finding is not the same as the absence of risk.

You can find out more about the challenges of fraud identification and prevention here

Key highlights:听

• • • The scale of risk demands urgent attention 鈥� The World Cup’s five-week span across three nations creates a human trafficking risk profile far beyond any previous North American sporting event.

    • Geographic exposure extends far beyond host cities 鈥� Unlike the Super Bowl, where risk is concentrated in one metro area, the World Cup’s national identity-driven fan engagement means every city in the US, Canada, and Mexico is effectively a participant city.

    • Cross-sector preparation is the most critical investment 鈥� Cutting down siloed operations among law enforcement, financial institutions, and NGOs is required, that means establishing financial institution task forces, training frontline bank branch employees to recognize trafficking indicators, sharing cross-sector information, and amplifying public awareness campaigns before the tournament begins is crucial.

The 2026 FIFA World Cup will be the largest sporting event ever hosted on North American soil, a tournament with 104 matches spanning more than five weeks across three nations and drawing an estimated 6.5 million visitors from around the world. While the United States hosts large sporting events like the Super Bowl each year, the World Cup brings with it the unique challenges of length of time, fan influx from around the globe, and geographic expansion.

Assessing the scale of human trafficking risk

To understand the magnitude of the human trafficking risk involved in events such as this, it is useful to apply a framework that accounts for three variables: i) the likelihood of a trafficking event; ii) the potential extent of damage; and iii) the duration of exposure. When that framework is applied to the 2026 World Cup, the human trafficking risk associated with the event registers high due to numerous factors.

For more on this, tune into the 成人VR视频 Institute’s latest “Clarity” podcast

The most significant differentiating factor of the World Cup is its time duration. The Super Bowl is a single-day event, and the Olympics run approximately two weeks. The 2026 World Cup spans more than five weeks across three nations, a duration that has no modern sporting equivalent. The last three World Cups, held in Brazil, Russia, and Qatar, offer limited comparative value given the substantial differences in legal frameworks, cultural contexts, and infrastructure. For purposes of risk assessment, this is why the Super Bowl represents the most relevant domestic benchmark, even though it falls considerably short as a true comparison.

Human trafficking evidence from the most recent Super Bowl

The most recent Super Bowl, held in the San Francisco Bay Area in February 2026, illustrates the scale of the human trafficking challenge. A coordinated anti-trafficking campaign conducted across 11 Bay Area counties resulted in the recovery of 73 sex trafficking victims, including 10 minors, and 29 arrests, all in connection with a single-day event.

Sex advertisement data from that period further substantiates the scale of human trafficking concern. In the months preceding the event, advertisement volume rose steadily before spiking dramatically during Super Bowl weekend and declining sharply in the days that followed. Analysis that was restricted to advertisements referencing the Super Bowl by name showed trend lines that remained essentially flat until the event itself, at which point volume surged significantly.

Likewise, examination of phone numbers associated with those advertisements revealed organized and purposeful movement. Nearly 500 unique numbers that had posted sex advertisements in other states in the preceding weeks appeared in San Francisco during the event.

The risk of human trafficking expanding beyond the host city is one additional insight uncovered during the anti-trafficking operation during the Super Bowl. Advertisements referencing the Super Bowl spiked simultaneously in Boston and Seattle, the home cities of the two competing teams. In the context of the World Cup, every city in the United States, Mexico, and Canada is effectively a participant city, and national identity rather than team affiliation drives fan engagement. The geographic distribution of risk is therefore exponentially greater than anything observed around the Super Bowl.

Hotspots of sex ads

What anti-trafficking partners should do now

Those organizations and institutions that take action in advance of the World Cup will be substantially better positioned to detect exploitation and protect vulnerable individuals. More specifically, these organizations should:

• Establish financial institution task forces in advance of the event 鈥� Convening local financial institutions to align on existing practices and identify gaps will aid in ensuring all parties are on the same page. It also establishes relationships and procedures that cannot be built effectively during a five-to-six-week surge in cross-border transactions. Activating established information-sharing mechanisms, such as the processes supporting the filing of and the , will be essential for detection and pattern recognition.
• Institute branch-level employee training at local financial institutions 鈥� Frontline employees possess local knowledge that no centralized system can replicate. A branch employee in a high-traffic urban location understands the patterns of their customer base and is often the first to recognize when something is amiss. What they frequently lack is the context in which to interpret that instinct and the guidance to act upon it. Addressing that training gap before the World Cup represents one of the highest-value preparedness investments available to financial institutions at this time.
• Dismantle institutional silos 鈥� Siloed operations, in which law enforcement, financial institutions, and non-governmental organizations (NGOs) each operate independently, represent the least effective organizational posture for an event of this scale. Institutions that establish cross-sector relationships and information-sharing commitments in advance will be meaningfully better equipped to respond.
• Develop and amplify public awareness campaigns 鈥� Research demonstrates that sustained public awareness campaigns and visible law enforcement presence reduce demand. Host cities, law enforcement agencies, and NGOs should treat this as actionable guidance in planning their response strategies.

The 2026 FIFA World Cup is not simply another major sporting event. The institutions, agencies, and organizations that approach it as such will find themselves unprepared for a scale of human trafficking risk that North America has never previously encountered.

You can find more about the resources, tools, and information that cities and organizations need to address听human trafficking around large-scale sporting events at听the 成人VR视频 Institute鈥檚 Large-Scale Public Events Toolkit here