They say “necessity is the mother of invention,” and in Latin America, necessity runs deep. Over one‑quarter of the population still lives outside the formal financial system; families in remote Andean and Amazonian towns are hemmed in by their sheer distance away from nearby cities, with mountains breaking up supply chains and denying them vital access to medication or counseling. Nevertheless, these gaps have become canvases for entrepreneurs. Rappi’s yellow backpacks coursing through Bogotá alleys and Nubank’s purple cards in Brazilian favelas are proof that creativity plus code can turn structural pain points into billion‑dollar solutions. Investors have followed this energy, betting that the next global platforms can rise from places where the need for change is visceral, everyday, and impossible to ignore.

A decade and a half ago Latin American founders confronted three almost‑universal headwinds: venture capital (VC) was scarce, incorporation and exit rules were labyrinthine, and the most skilled engineers were lured abroad by higher salaries and deeper capital pools. A World Bank review of the period notes that foreign funds—when they showed up at all—dominated the largest deals because few domestic financial institutions were willing or even allowed to back VC vehicles. Local startups were left under‑financed and over‑regulated.

Even so, a handful of breakout companies began to prove that scale and liquidity events were possible. Mercado Libre’s 2007 Nasdaq IPO, which raised USD 289 million, demonstrated regional e‑commerce could be both profitable and investor‑friendly. Globant’s 2014 debut on the New York Stock Exchange signaled that a home‑grown software firm could compete globally and tap U.S. public markets. Despegar’s 2017 listing extended that narrative into online travel. This milestone excited capital and talent back into the ecosystem and, just as importantly, reassured international investors that Latin America offered real paths to liquidity.

As these companies flourished, regional policymakers began launching reforms that rewired the startup ecosystem’s legal plumbing. Recent statutory overhauls have made it far easier—and safer—for capital to flow into young companies. Brazil led the charge with the Marco Legal das Startups (Law 182/2021) created explicit limited-liability shields for passive angel and VC investors, opened a regulatory sandbox for testing new technologies, and streamlined public-procurement rules so startups can sell to government without prohibitive red tape. Mexico matched that spirit with its S.A.P.I. corporate form—a venture-friendly shell that allows for active and passive investors to take on different corporate governance roles—and then layered on the Fintech Law of 2018, which complements the corporate S.A.P.I. shell by giving two clear National Banking and Securities Commission licence tracks: IFPE (electronic-payment institutions, i.e., e-wallets) and IFC (crowdfunding platforms). Startups now have clarity on compliance requirements and predictable approval timelines, significantly easing fundraising and regulatory processes. Colombia’s earlier SAS model (Law 1258/2008) cut incorporation times from months to days; Argentina (2017) and Uruguay (2019) later copied this template, creating a near‑uniform playbook for cross‑border deals. Collectively, these reforms have given global investors the safety they were missing fifteen years ago.

Against this backdrop of regulatory progress and early successes, Latin America was perfectly positioned when global liquidity surged in 2021: Brazil and Mexico captured more than half of the total USD 16 billion that startups raised that year. Cheap money, pandemic‑driven digitization and a fear of missing out pulled megafunds like SoftBank and Tiger Global into ten‑figure rounds for Nubank, Kavak and Rappi.

Global macro conditions flipped in 2022. Rapid U.S. interest-rate hikes and recession fears drove investors toward safer assets, pulling Latin-American venture funding down from USD $7.9 billion in 2022 to about USD $4 billion in 2023. Yet LAVCA characterises this pull-back as a consolidation, not a collapse: 2023 deal counts still outpaced pre-pandemic norms, and early-stage rounds captured 42 % of all VC dollars, signalling that appetite for new bets remains healthy. Meanwhile, LatAm-focused fund managers raised roughly US $2.1 billion for new vehicles in 2023—the region’s third-largest haul ever—adding home-grown dry powder to the ecosystem. Analysts at Crunchbase and TechCrunch note that the tighter market has pushed founders to prioritise unit economics and a clear path to profitability over “growth at any cost,” a shift many believe will make the next cycle more resilient.

Despite this significant progress, some critical headwinds remain strong enough to threaten the region’s growth momentum. First is macro volatility: inflation spikes and rate hikes in the market have led regional VC firms to shy away from making major investments, with the USD 16 billion raised in 2021 dropping to just USD 4 billion in 2023. Second, a late‑stage capital gap has opened; with crossover funds on the sidelines, many scale‑ups turned to venture debt, which still topped USD 1.2 billion across 47 Latin deals in 2022. Third, fragmented regulation and patchy infrastructure raise the cost of regional expansion; rural broadband penetration in some countries is less than half the urban rate, capping digital‑services growth. Finally, a relentless talent drain—fueled by dollar‑denominated remote offers from U.S. and EU firms—forces local startups to compete on salary before equity culture is fully accepted.

Looking ahead, the path forward is clear. First, broadening the late-stage investor base by inviting local pension funds and insurers to invest in startups will create a stable capital foundation, reducing dependence on unpredictable international funding. Second, achieving regulatory harmonization, especially in fintech, will allow startups to scale seamlessly across Latin America’s diverse markets. Third, clarifying and codifying equity-compensation rules will transform stock options into a compelling retention tool, anchoring talent locally. Finally, strategic investments in digital infrastructure, combined with simplifying bureaucratic procedures, will dramatically expand market reach and accelerate growth, ensuring ideas swiftly translate into real-world impact. If these milestones are met, the next generation of Latin American founders will secure both the funding and the talent they need—without ever needing to book a ticket to Silicon Valley.

Ultimately, Latin America’s strength lies in its people—the entrepreneurs who know firsthand the challenges they seek to overcome. Time and again, they’ve transformed adversity into innovation, turning everyday obstacles into global opportunities. With continued support, strategic reforms, and a shared commitment to progress, there’s every reason to believe the coming decade will witness unprecedented growth, propelled by the spirit, ingenuity, and unwavering resilience of those who proudly call this region home.

Investor money constantly flows into the tech industry, especially emerging AI companies. Recently, corporate investors have funded top startups in the AI space far more often than they have in other sectors. As of 2021, corporate investors have comprised 16 percent of all investment in the AI industry. Big investment banks have even created investment companies that specialize in these technologies, helping customers invest in companies specialized in market infrastructure, information services, security software, mobile technology, big data analysis, payments, and more. For example, Goldman Sachs’s Principal Strategic Investments Group serves customers worldwide.

As per Goldman’s 2024 investment commentary, despite macroeconomic concerns, the tech sector continues to show resilience due to improving fundamentals, AI advancements, and easing rate expectations. Specifically, as AI opportunities grow, companies under $100 billion in market cap have become increasingly optimistic. AI infrastructure has been a key return driver, and despite slower-than-expected AI adoption, market enthusiasm for AI remains strong. However, it is essential to note that even as businesses sign and negotiate longer contracts to shield themselves from prospective AI risks, there is little certainty in determining what laws apply to these emerging technologies. Moreover, AI systems are not entirely accurate and are vulnerable to making arbitrary mistakes, leaving an open question around whether it is possible to build AI models that comply with potential regulation at all.

One of the solutions to potential AI noncompliance discussed in proposed legislation is what’s called “system shutdown.” System shutdown is a method of mitigating the harms that might be caused by noncompliant AI being used to engage in illegal activity. Proposed legislation, such as California’s SB 1047, floated the idea of forcing all large-scale, “high-risk” AI models to include a “kill switch” that would unilaterally shut down the AI model if it was detected to pose “serious risks to humanity.”

Governor Newsom ultimately vetoed SB 1047. As the bill was being debated, Silicon Valley companies (including OpenAI) emphasized how this controversial AI bill would stifle innovation. It is unclear whether this push stemmed from general inefficiencies regarding these “system shutdown” solutions or these companies’ broader fears of business shutdown. Noncompliance with a system shutdown policy could certainly lead to (i) breach of covenants with investors and (ii) prolonged legal battles between investors and founders. However, for emerging AI companies, system shutdowns could also fatally disrupt day-to-day operations and growth. Generally, AI models are developed (i) with certain reliance on third-party platforms like OpenAI’s API and/or (ii) in-house entirely. Hypothetically, a shutdown affecting a key partner like OpenAI could sabotage dependent companies, while regulatory action could derail companies ignoring compliance from the start. Therefore, in both situations, the compliance risks are significant.

That said, other alternative solutions to potentially risky AI face similar dilemmas. The EU AI Act talks about the “withdrawal of non-compliant AI systems from the market,” which ultimately raises investor concerns about whether a company’s stance towards the incredibly complex issue of AI product compliance could lead to the loss of their investment entirely. The Colorado AI Act creates strict rules and standards for developers and deployers of high-risk AI systems to follow, adding more responsibility to AI model developers and giving investors a potentially critical due diligence question to seek answers for. This approach runs into a similar barrier as California’s vetoed SB 1047 did: Is it possible to fully capture all “high-risk AI systems” in a single regulation, especially as technologies change so quickly and companies are looking for newer ways to innovate in such a lucrative field?

At the 2024 Seoul AI Safety Summit, major players like Microsoft, Amazon, and OpenAI, amongst others from the U.S., China, Canada, the U.K., France, South Korea, and the UAE, agreed to set up voluntary commitments concerning AI safety and ethics advancements. The participants pledged to create safety frameworks for mitigating the challenges associated with their frontier AI models, while addressing potential risks caused by the progress of AI, such as cyberattacks, the development of bioweapons, and the misuse of technology by malicious actors. While these are important commitments to make as AI becomes more prevalent, there are still open questions about the potential ineffectiveness of expensive penalties like system shutdowns and whether different regulatory policies might create more effective enforcement actions in their place.

With the fast pace of technological growth and the trial-and-error approach to regulation, navigating contractual obligations in commercial and transactional spaces remains a key strategic focus area. Given how risky developing AI models can be and how investors lack significant certainty in the safety of their investments when they put money into AI startups, legal compliance will increasingly play a crucial role in navigating investor expectations. With proposed legislation potentially implementing product withdrawal or shutdown requirements, investors and companies face huge risks and financial burdens in this growing field. Therefore, closely monitoring compliance requirements as they arise, adhering to them promptly, and building stronger corporate strategies in collaboration with key stakeholders, including investors, are essential to keeping the business running smoothly.

The New York Climate Change Superfund Act, signed into law on December 26, 2024, is a legislative measure designed to address the financial impacts of greenhouse gas emissions from specific industry actors. The Act mandates that businesses engaged in fossil fuel extraction or crude oil refinement, with a documented emission history of one billion metric tons or more between 2000 and 2018, be obligated to pay for climate change-related damages. The fee is calculated based on the business’s proportional share of greenhouse gas emissions within the designated time frame. The businesses will pay upwards of $3 billion annually for up to 25 years. The collected funds are to be deposited into a designated “Climate Superfund” and allocated towards the restoration and protection of New York wetlands and infrastructure, as well as the support of public health programs addressing climate change-related illnesses and injuries. This landmark Act not only confronts the environmental and public health costs of fossil fuel emissions but also sets the stage for an important legal battle that could redefine the boundaries of corporate accountability in climate policymaking.

The Act has sparked intense legal and political debate, drawing both profound support and disapproval from various stakeholders across the country. One of the many parties in opposition to the Act is a coalition led by the U.S. Chamber of Commerce, which has filed a lawsuit in federal court to block its enforcement. They argue that the Act is unconstitutional, asserting that New York has exceeded their outreach by attempting to regulate interstate pollution and energy policy, traditionally reserved for the federal government. Furthermore, the coalition alleges that the Act imposes unlawful retroactive liability on businesses by penalizing them for actions that occurred over the preceding 25 years. They also suggest that the Act will have ripple effects across various stakeholder groups beyond the direct financial implications for fossil fuel companies. It is inevitable that the financial burden placed on fossil fuel companies by this legislation will be passed down to consumers through raised oil and gas prices. The coalition does not stand alone in any of its allegations in this legal battle, as 22 states have also joined in filing suit against New York for this Act.

Conversely, those in support of the Act, such as Senator Liz Kruger, offer a contrasting perspective. They posit that the legislation is a necessary and equitable measure to address the costs and damages of climate change, arguing that those entities most responsible for, and profiting from, activities contributing to these damages should bear the associated financial responsibility. They further assert that the Act is a deterrent, incentivizing the fossil fuel industry to pursue emissions reduction strategies. In response to constitutional challenges, they contend that the Act represents a valid exercise of the state’s police power to safeguard the environment and public health, rather than an overreach into interstate commerce. Finally, they argue that the opposition’s suggestion that the Act will increase oil and gas prices is fundamentally flawed, asserting that global market dynamics control these prices, not state legislation. Many environmental justice advocates are in support of this Act as well, characterizing it as a crucial step towards addressing the historical injustices faced by communities, including low-income neighborhoods and communities of color, which have disproportionately borne the brunt of pollution from the fossil fuel industry. The Act acknowledges the need to rectify these past harms by directing funds towards remediation and public health.

The New York Climate Change Superfund Act serves as a call to action for other states and the federal government to adopt more aggressive climate policies, showing that state governments will be willing to act, even when the federal government will not. The legal challenges to the Act will have significant implications for the future of climate regulation. It is a complex and controversial legislation, and it is unclear whether or not the courts will uphold the Act. A ruling in favor of New York could embolden other states to enact similar legislation, while an adverse decision could limit the scope of state authority in this area. However, regardless of the outcome, the Act has already sparked an important debate about the role of the fossil fuel industry in climate change and the need for states to address this issue.

2020 and 2021 brought one of the largest Initial Public Offering (IPO) booms in US history, brought about by an increased interest in digital technologies during the COVID-19 pandemic. However, in 2022, this market stagnated because of worsening market conditions and a Fed-sponsored contractionary monetary policy. Once inflation slowed down, the stock market rebounded considerably, leading the Federal Reserve to cut interest rates. This resulted in market volatility returning to pre-pandemic levels in 2024. As such, analysts predicted that this company-friendly environment would trigger a resurgence of IPOs by 2025.

However, a disappointing first quarter of 2025 has cooled that excitement. Much of last year’s initial excitement was based on a spike in IPOs in 2024. Additionally, the newly elected administration promised in their campaign to deregulate the IPO market and thus encourage investment growth.

However, this narrative overlooked the importance of the tech sector in the IPO market. In 2024, the healthcare sector led the number of IPOs with 43% of all IPOs, but these IPOs only brought in 26% of the total capital invested. On the other hand, the tech sector accounted for 18% of the total number of IPOs, with 23% of the total capital invested. This disparity highlights that tech firms attract a disproportionate amount of investment – and were the real drivers behind the upswing in IPO capital raised in 2024.

Recently, however, investor confidence in tech startups has wavered, mainly due to the volatility of that market. Investors fear overexcitement in a sector that has brought forward the most significant IPOs (such as Facebook and Alibaba), but also some of the worst (with Rivian and Robinhood). As such, tech startups have remained private longer to consolidate their financial records and wait for the right opportunity to go public.

Additionally, major tech companies such as OpenAI have been able to raise enough capital through private markets instead of being forced into a volatile public market that might reduce their valuation. As a result, the IPO market has lost one of its primary drivers of large, high-profile deals. With a lack of substantial tech listings, the predicted rise in 2025 IPO investment appears uncertain, driven more by traditional sectors offering modest returns than exciting tech debuts.

Not only that, but there was a significant chilling effect on IPOs because of the number of tech IPOs conducted in 2021 that opted to circumvent U.S. Securities and Exchange Commission (SEC) regulations through a special reverse merger structure. Tech companies looking to go public would reverse-merge a private firm with a public shell company incorporated solely for the purposes of the merger (named a “Special-Purpose Acquisition Company,” or SPAC). The lack of regulatory scrutiny created by the SPAC reverse merger structure generated much controversy amongst investors. It effectively created a loophole in the disclosure obligations regulated by the SEC to safeguard investors from potential share pricing fraud. Consequently, fraud accusations (such as Rivian’s IPO) and poor performance on these IPOs (by 2022, 80% of 2021 IPO’s still hadn’t seen positive earnings and 33% were trading below their opening price) led to investors seeking alternative methods of investments. This created further mistrust between public offerors and potential investors. Thus, even before the SEC regulated this loophole, investments in reverse-merger IPOs had dropped by 93%.

Finally, the macroeconomic landscape does not seem inviting for a public offering led by volatile tech firms. Investors expected the new administration officials to remove Biden-era rules and descale what was perceived as overreaching regulations. The new administration still insists they will conduct such deregulation, but until they do, Biden-era regulations are still enforceable. For now, investors are stuck waiting on whether the administration will deliver their deregulation promises and wondering if it’s worth betting on such rollbacks. This hesitation has only grown with the administration’s recent moves, which increase uncertainty in an already volatile market.

A new trade war has sent markets into a tailspin from which it still has not recovered. Economists have been reluctant to predict a recession, but the Fed has rejected further lowering interest rates, fearing that the administration’s trade war would result in higher inflation. As a result, the market volatility index increased by 52% in the last month, meaning that investors are less likely to invest because of high risk and low returns. If a tech startup wanted to conduct an IPO under such conditions, it would have to sacrifice the expected valuation of its shares. Thus, raising capital will become more expensive, discouraging potential investors.

Ultimately, while early signs in 2024 sparked hopes of an IPO resurgence, the reality has proven far more complex. The absence of significant tech listings, lingering regulatory hurdles, and rising geopolitical uncertainty have all combined to dampen investor enthusiasm. For now, the IPO market appears to be entering a more cautious and selective phase—one where traditional sectors lead modest recoveries. However, the explosive tech-driven growth of past years remains out of reach. Until investor confidence returns, and regulatory clarity emerges, the IPO landscape will likely stay sluggish, especially for tech startups that once used IPOs to fuel their most lucrative deals.

As globalization accelerates, effective transfer pricing strategies have become crucial in cross-border mergers and acquisitions (M&A). Transfer pricing sets the prices for transactions between affiliated entities within the same corporate group, aiming to allocate profits based on the arm’s-length principle—treating intercompany transactions as if they were between unrelated parties. Inaccurate transfer pricing can lead to tax investigations, penalties, and significant damage to a company’s financial health and reputation. Proper profit distribution across operations is essential for foreign-owned companies to navigate tax regulations. Non-compliance may lead to tax liabilities, as tax authorities could claim improper profit shifting to low-tax jurisdictions. Effective transfer pricing strategies are therefore essential for multinational corporations involved in cross-border deals.

Section 482 of the U.S. Internal Revenue Code mandates that transactions between related companies must occur at market prices. This section establishes the legal framework for making transfer pricing adjustments, requiring U.S. companies to document intercompany transactions thoroughly. In the U.S., transfer pricing documentation is mandatory, placing the burden of proof on companies to demonstrate compliance during tax audits. The Organization for Economic Cooperation and Development (OECD) offers internationally accepted guidelines for transfer pricing, widely used by tax authorities globally. While U.S. regulations align largely with these guidelines, there are subtle differences that foreign companies must be aware of when operating in the U.S.

When a U.S. company is acquired, integrating its operations and supply chain often requires revisiting intercompany transaction prices to reflect changes in business structure. For example, shifting raw material or product prices directly affects transfer pricing, making it essential to reassess prior arrangements post-acquisition. One of the most challenging aspects of transfer pricing in M&A is valuing intangible assets such as brand value, patents, trademarks, and proprietary technology. Inaccurate valuations can lead to adjustments by tax authorities, resulting in additional tax liabilities.

OECD guidelines outline five primary transfer pricing methods used to determine arm’s-length pricing and these form the foundation of international best practices.

U.S. regulations, under Section 482 of the Internal Revenue Code and the associated Treasury Regulations, generally align with these methods, particularly the Comparable Profits Method (CPM), which is widely applied in the U.S.

However, there are important distinctions. For instance, while the OECD treats all five methods as potentially equal in applicability depending on the facts, the U.S. regulations emphasize the “best method rule,” which requires taxpayers to use the most reliable method based on the available data.

Moreover, the U.S. tends to favor methods that rely on publicly available comparable data, like CPM, whereas the OECD may place greater emphasis on methods like the Profit Split Method in cases involving valuable intangibles or highly integrated operations.

Understanding these nuances is critical for foreign multinationals acquiring U.S. targets, as they must ensure that their transfer pricing strategies comply not only with international norms but also with specific U.S. regulatory expectations.

The OECD’s five methods fall into two categories: traditional transaction methods and transactional profit methods. Traditional transaction methods focus on comparing specific terms or prices of transactions between related companies with independent ones, while transactional profit methods focus on comparing the overall profits of related companies with those of independent companies.

Traditional Transaction Methods

• Comparable Uncontrolled Price (CUP) Method
  The CUP method compares prices between related companies with prices between unrelated companies. This method is most effective when there are sufficient comparable transactions between independent entities to establish a benchmark. For example, a U.S. car rental company may price the use of its brand for a Canadian subsidiary by comparing it to an independent deal with a third-party car rental company.
• Resale Price Method (RPM)
  The RPM uses a product’s resale price and subtracts a gross margin based on comparable transactions. It is commonly applied when goods are purchased from a related party and resold to an independent third party. For example, a U.S. shoe distributor may buy from an Irish affiliate and compare the price to that of an unrelated supplier. The gross margin from the unrelated supplier sets the price for the related party.
• Cost Plus Method (CPLM)
  The CPLM adds a market-based profit margin to the cost of producing goods. It is typically used for routine manufacturing or distribution activities. For example, a French company selling goods to its German parent may apply a markup based on comparable market transactions.

Transactional Profit Methods

• Comparable Profits Method (CPM)
  The CPM compares the net profit from a controlled transaction to the net profits from similar transactions between unrelated companies. This method is widely used and relatively easy to apply when financial data is available. For example, a U.S. clothing company with a Canadian distributor may compare the Canadian distributor’s profit margins to those of similar companies in Canada.
• Profit Split Method (PSM)
  The PSM is used when related companies collaborate on a joint project or product and split profits based on their contributions. For instance, a pharmaceutical company and its R&D affiliate may agree to split profits based on the investments each entity made in developing a new drug. While useful for highly integrated businesses, the PSM can be subjective and difficult to apply, potentially leading to disputes with tax authorities.

The case of Coca-Cola Co. v. Commissioner of Internal Revenue highlights the complexities of transfer pricing. Coca-Cola contested the IRS’s retroactive decision to change the transfer pricing method it had been using for years. Initially, the IRS approved the “10-50-50” method for allocating profits between Coca-Cola and its foreign affiliates, allowing affiliates to profit on 10% of gross sales before splitting the remaining profits “50-50” with Coca-Cola. However, the IRS later switched to the Comparable Profits Method (CPM), reallocating a significant portion of income back to Coca-Cola and increasing the royalties owed by affiliates to Coca-Cola. Coca-Cola argued that the change was arbitrary and violated administrative law principles. The IRS, however, emphasized that the affiliates lacked ownership of key intangible assets like trademarks, which Coca-Cola solely owned. The application of CPM, which compared the profitability of Coca-Cola’s affiliates to independent bottlers, aimed to allocate profits more accurately according to the arm’s-length principle. The Tax Court upheld the IRS’s decision, stating that the switch was appropriate. This case underscores the unpredictability in transfer pricing assessments and the challenges multinational corporations face when tax authorities contest established methods.

To mitigate transfer pricing risks in cross-border M&A transactions, companies must establish clear and transparent pricing policies that comply with domestic and international tax regulations. Best practices include maintaining thorough transfer pricing documentation, conducting regular audits, and staying updated on regulatory changes. Additionally, foreign-owned companies should periodically reassess intangible asset valuations and global pricing strategies to ensure compliance with evolving tax laws.

Integrating transfer pricing considerations into the M&A due diligence process is also crucial. This will ensure that any pre-existing transfer pricing risks or issues are identified and addressed before the deal is finalized. By taking proactive measures to manage transfer pricing risks, multinational companies can minimize the likelihood of tax disputes and position themselves for long-term success in the global market.

In conclusion, effective transfer pricing strategies are vital to successful cross-border M&A transactions. By understanding the complexities of international tax regulations and choosing the appropriate transfer pricing methods, companies can navigate the risks, ensure compliance with the arm’s-length principle, and avoid costly disputes. These strategies will ultimately help companies mitigate tax risks and achieve sustainable success in an increasingly interconnected global marketplace.

Delaware has held a dominant position in corporate law for more than a century. When New Jersey liberalized its incorporation statute in the late 19th century, it successfully attracted new incorporations and states raced to deregulate. Delaware adopted much of the New Jersey statute as its general corporate law in 1899. However, in 1910, New Jersey reversed course and altered its corporate code to be less business-friendly, so companies moved to reincorporate in Delaware to take advantage of its nonregulatory enabling statute. Until recently, companies haven’t looked back.

Despite being the second smallest state in the United States, more than two-thirds of Fortune 500 companies are incorporated in Delaware. Corporations are a significant part of Delaware’s budget, with the annual “franchise tax” accounting for more than 12 percent of revenues to the state. The State of Delaware attributes its significance in corporate law to its ability to provide “managers and investors with laws optimal for engaging in ethical and profitable business.” The State cites five factors as contributing to Delaware’s preeminent place in corporate law:

(1) The Delaware General Corporation Law’s (DGCL) statute, which contains only a few mandatory requirements to protect investors and otherwise offers corporations full flexibility to run their business.

(2) The Delaware court system, including its specialized, jury-free, bipartisan Court of Chancery with jurisdiction over corporate disputes.

(3) Delaware’s case law and its extensive body of favorable precedent, including its “business judgment rule” (that courts shouldn’t second-guess business decisions made in good faith and with due care).

(4) The array of expert attorneys in Delaware corporate law who help the Delaware legislature by reviewing and recommending changes to Delaware corporate law.

(5) The Delaware Secretary of State’s office, which offers corporations expedited services for time-sensitive matters.

However, in recent years, a widely shared perception has emerged that Delaware courts have become more receptive to shareholder litigation, leading some companies to consider reincorporating outside of Delaware. This perception has been matched with recent moves by other states – specifically Texas and Nevada – to attract businesses to incorporate and invest in their states. In combination with several high-profile reincorporations, some outside observers question the longevity of Delaware’s preeminent place in corporate law.

In August 2024, Texas launched a new Texas Business Court in an attempt to create a forum similar to the Delaware Court of Chancery. The court comprises eleven geographic regions, five of which are currently operational, and seeks to resolve certain complex business disputes. As enacted in Texas’ House Bill 19, the court’s jurisdiction includes corporate governance actions, claims related to state or federal securities laws, actions seeking to hold directors or officers liable, and other actions arising from transactions. According to a recent Op-Ed in the Wall Street Journal from Texas Governor Greg Abbott, Texas’ legislature is also considering amendments to its Business Organizations Code to codify the business judgment rule and set ownership limitations for derivative suits.

Likewise, Nevada has also promoted itself as an attractive destination for corporations. Nevada’s legislature has sought to distinguish the state’s corporate law statutes from Delaware’s for decades by offering corporate managers greater protection than Delaware. Later this year, Nevada’s legislature will also consider a constitutional amendment to establish a business court with exclusive jurisdiction over business-related disputes. Judges on the court would be appointed by Nevada’s governor to six-year terms and chosen from a list of nominees provided by a judicial selection commission.

While it is too soon to know if these changes will lead to an exodus from Delaware, several high-profile reincorporations illustrate the need for Delaware to take these changes seriously. For example, since 2024, Elon Musk has moved three companies – SpaceX, Neuralink, and Tesla – to Texas. This was prompted in large part by multiple adverse outcomes before Delaware Chancery Judge Kathaleen St. J. McCormick. In 2022, Chancellor McCormick rejected Musk’s efforts to back out of purchasing Twitter. More recently, Musk appeared before McCormick in a shareholder lawsuit alleging that Tesla’s directors breached their fiduciary duties by awarding Musk a $55.8 billion compensation package. Writing for the Chancery Court, McCormick criticized the board’s process, conflicts of interest, and Musk’s compensation amount, before ultimately rescinding Musk’s compensation plan. In response, Tesla shareholders voted to re-approve Musk’s compensation package and support Musk’s proposal to move the company’s legal home to Texas. Musk then posted on X: “Never incorporate your company in the state of Delaware.”

Additionally, a February 2025 Delaware Supreme Court ruling in Maffei v. Palkon could pave the way for more companies to reincorporate out of Delaware. The case arose from TripAdvisor’s reincorporation from Delaware to Nevada, which the plaintiffs alleged was a self-interested transaction because Maffei provided the decisive votes in favor of reincorporation. The decision applied the business judgment rule, allowing the reincorporation to proceed because no board member received a material benefit. The decision provided needed legal clarity for companies considering reincorporation, which could lead to additional reincorporations in the future.

It isn’t yet clear how many other companies may choose to reincorporate out of Delaware. The Wall Street Journal recently reported that Meta is also considering moving its legal residence out of Delaware, with the company apparently weighing the benefits and drawbacks of reincorporating to Texas. While Meta’s motivations aren’t certain, commentators have speculated that shareholder litigation against Meta in Delaware may be a factor. Meta potentially leaving Delaware on the heels of Tesla’s reincorporation could represent a shift that could have significant reputational repercussions for the State of Delaware.

In response, Delaware Governor Matt Meyer is weighing unilateral changes to Delaware’s chancery court system. Meyer highlighted how the Chancery Court is designed to assign one judge all subsequent cases involving the same company. While intended to improve the court’s efficiency by allowing judges to use their prior knowledge of the company to expedite litigation, companies may feel they aren’t getting a fair opportunity when repeatedly appearing before the same judge (particularly if that judge has already ruled against them, as demonstrated by Elon Musk’s criticism of Chancellor McCormick).

Most notably, however, the Delaware state legislature recently enacted Senate Bill 21 to quell additional corporate defections from the state. Signed into law on March 25, the legislation provides a safe harbor for directors or officers, as well as controlling stockholders like Musk and Zuckerberg, who have interests or relationships that might not normally render them independent in a transaction. The changes to the DGCL will protect directors or officers if their actions were approved or ratified by a majority of disinterested directors or stockholders (something that may have resulted in a different outcome for Musk’s compensation package). Further, the legislation sets forth new limitations and procedures that stockholders must follow to inspect a corporation’s books and records, which could reduce stockholder’s influence over a corporation. Senate Bill 21 passed the Delaware Senate with the Governor’s backing 20-0 on March 13, passed the Delaware House of Representatives 32-7 on March 25, and was quickly signed into law.

While Tesla and TripAdvisor were highly visible reincorporations, Delaware’s swift response to amend their corporate laws, and other companies’ relative caution, suggest that Delaware’s place of preeminence won’t change anytime soon – despite other jurisdictions’ efforts to seize Delaware’s longstanding role as the top state for corporate law.

Sports representation by Big Law firms is by no means a novel concept: Traditional Big Law giants and American sports league staples have long partnered on sports deals ranging from contract negotiations to dispute resolution. But the deeply established partnership of the two traditional powerhouses is being confronted by both a transformative investment and athletic landscape: private equity and emerging non-traditional sports leagues. Amid the recent sports deal boom, a question unique to these recent trends remains: Which Big Law players will represent which sports leagues?

The explosion of sports transactions this past year—despite general M&A down-trending—is a testament to the growing legal sports sphere. However, this deal increase has been less dominated by the established sports Big Law firms—Latham & Watkins, Hogan Lovells, Covington & Burling, to name a few—than in years past. Deeply traditional firms such as Cravath, Swaine & Moore and Wachtell, Lipton, Rosen & Katz have entered the sports deal world, existing as competitors to the cemented sports deal firms and reflecting the sports transaction growth. In late 2023, Cravath represented the Snyder family, billionaire heirs of the In-N-Out fast food restaurant chain, in its sale of the Washington Commanders. Wachtell, along with Hogan Lovells, advised on a complex deal investing $3 billion into the Professional Golf Association this past June. The sports industry newcomers have expressed little hesitation in dealing with sports transactions despite ostensible hurdles: Marke Greene, head of the corporate department at Cravath, shared that the firm “did not have an expert and . . . figured [the NFL league rules] out just fine.” But although new entrants into the industry appear to have quickly gained deal traction, traditional firms have touted their “competitive advantage” in expertise of sports teams sales while also recognizing the growing competition for sports-related deals.

Today’s sports transactions are less crowded with the traditional sports powerhouses of the Major League Baseball, National Basketball Association, and National Football League. Less established leagues have gained a foothold in sports representation, reflecting the public’s, and consequently the legal field’s, embracement of the newfound popularity of non-traditional sports. Since June, Latham & Watkins has represented the private equity firm Carlyle in its investment in the National Women’s Soccer League club Seattle Reign FC as well as a deal concerning UpShow, a digital software that assists in selling companies the rights to stream NFL Sunday. Hogan Lovells has similarly embraced the modern sports landscape, being involved in a deal concerning a virtual golf circuit founded by Tiger Woods.

But what is the source of all this newfound sports transaction business? The true origin of the uptick in “riskier” sports investments is the growing portfolios of large private equity firms. The firms have had huge financial incentive to tackle the sports industry—with many sports teams outperforming the S&P 500—and have embraced the industry’s modernization. With investment in the traditional sports leagues exhaustive, private equity firms have set sights on new, modern sports transactions, often focusing on both the sports and technology industries. Silver Lake, a prominent sports private equity investor, acquired global entertainment and media company Endeavor Group Holdings for $13 billion last April, which owns the Unted Fighting Championship, World Wrestling Entertainment, Pro Bull Riding circuit, and Euroleague Basketball. With deal representation needed, several major law firms were also given a bite of the non-traditional Bull Riding apple. Private equity shows no signs of stopping their sports investment takeover, and major law firms have expressed little hesitation in getting involved. CVC Capital Partners, represented by Freshfields, invested $150 million in the Women’s Tennis Association, with dealmakers citing a goal to grow the size of women’s tennis in the nation. Investment firms’ interest in sports deals carries an inherent diversification of law firm sports representation, as major law firms have deep ties to private equity partnerships.

With private equity maintaining its reputation for high-risk, high-reward deals, the sports industry is the investment strategy’s likely new target. As a growing number of major law firms desire a chunk of the hotbed of sports transactions, it appears that private equity’s sports deal moves will reflect a larger trend towards a diverse, robust sports representation portfolio of Big Law firms.

More than 800,000 homes in California currently lack insurance coverage — 40% higher than the national average. As wildfires grow more frequent and severe, homeowners struggle to find insurers willing to cover their properties. The crisis became impossible to ignore after the Eaton and Palisades fires, when many residents found themselves displaced and uninsured. Public outrage quickly turned toward insurance companies, which were accused of abandoning homeowners in their time of need. Nevertheless, studies from the federal government and leading universities suggest that this trend began years before these fires started. They point to a different culprit: state-imposed price control regulation that makes it financially unsustainable for insurers to operate in high-risk areas.

Why? Insurance companies operate on probability and risk assessment. There is uncertainty in predicting the exact number of claims they will receive and have to pay out, so insurers rely on statistical models to adjust their premium prices. The insurers analyze past data and use probability to adjust that data for future trends. This allows insurers to offer premium prices that accurately reflect the risk associated with insuring someone’s life, property, land, and so on.

However, California’s Proposition 103 requires insurers to obtain government approval prior to increasing premiums. This is not an uncommon practice in other states. However, Proposition 103 goes further: it prohibits insurers from using forward-looking risk models that account for rising threats, such as climate change. This significantly impacts insurers’ ability to accurately allocate risk through the pricing of their insurance premiums in California. Proposition 103 forbids insurers from considering the increasing severity of wildfires, as they can only adjust their prices based on historical data. The problem arises as climate change has dramatically altered wildfire patterns — burn areas have grown fivefold since 1979, and the United Nations predicts a 57% increase in extreme wildfires by the end of the century. Nevertheless, because California law prevents insurers from pricing in this growing risk, the cost of insuring homes remains artificially low.

Insurance companies have had to reinvent their pricing structure to keep operating under this unsustainable business model. They began to raise the prices of premiums in lower-regulated states, subsidizing their operations in high-risk areas, such as California. This effectively and unfairly transferred the rising risk of climate change to homeowners living in less-risky areas, while artificially inflating home prices in those areas. However, this business strategy still doesn’t generate enough profit to allow insurers to operate in high-risk areas. Therefore, they have halted operations in areas where they have determined it is too risky to cover wildfires.

Another unexpected externality is that homeowners have been flocking to areas with a high risk of wildfires. There are two reasons for this. The first one is that home prices have been kept artificially low by insurance premium control measures that do not reflect the actual risk of wildfires. Attracted by low real estate prices, the demand for homes in high-risk wildfire areas has increased. The second factor is gentrification. As homes in high-wildfire-risk regions are destroyed, insurers are compelled to cover the costs of rebuilding. These rebuilds mean that homes tend to be newer, with better appliances. This works in conjunction with the previous factor: new homes in wildfire-prone areas are extremely attractive to new homeowners, as they are brand new and sold at low prices that overlook the risk of wildfires. Homeowners are being misled, and the California government ultimately puts the people it claims Proposition 103 is trying to protect in danger.

This does not mean that corporate greed should be left entirely uncontrolled by state regulation. Insurance companies operate as profit-maximizing businesses, and consumers need some degree of protection from this. However, California’s wildfire insurance crisis is a failure of policy that has distorted the way risk is priced and distributed. California should consider that its strict price control measures tend to dampen investment and growth. In this case, it even endangers possible homeowners. By forcing companies to rely solely on historical data, the law ignores the reality of a rapidly changing climate, leading insurers to withdraw from high-risk areas altogether. Without reform, what other possible remedies can the California government implement to address this crisis? It can only force insurers to operate in a market they are unwilling to or establish a state-sponsored insurance policy. Neither of these options looks sustainable in the long term.

To avoid further destabilizing its insurance market, California policymakers must permit insurers to utilize forward-looking risk models that account for climate change. The state’s focus should shift from suppressing premium increases to reducing actual wildfire risk—through stronger building codes, fireproofing incentives, and relocation programs for the most vulnerable areas. Without these reforms, the state risks deepening its insurance crisis: leaving more homeowners uninsured, more lives at risk, and an already fragile housing market on the brink of collapse.

The debate surrounding generative AI is not a recent phenomenon. While AI’s roots trace back to the 1950s, pioneered by Turing and Samuel, today’s explosion in generative AI has given rise to a much wider range of complex issues such as data harvesting, regurgitation, memorization and copyright. With companies harnessing vast datasets to train large language models (LLMs), legal battles in the US now center on the ‘fair use’ defense, with significant ambiguity surrounding the question of whether AI’s reliance on copyrighted material can ever be reined in without stifling innovation.

In the US, the infringement case defenses largely pivot on the doctrine of fair use as established by Section 107 of the Copyright Act. The fair use doctrine allows limited use of copyrighted material without permission for various purposes and Copyright Act outlines factors to determine fair use, including purpose, nature, amount, and market impact. The landmark case, Authors Guild v. Google, Inc. plaintiffs argued that verbatim copies of authors’ works were infringing, yet claims were denied as court held that artificial and nominal numbers of the original work were used in the reproduction. In this case, the defendant’s claim of 16% of original content used from the authors’ work was sufficient enough to meet the definition of a transformative purpose under the fair use doctrine. Authors Guild highlights the inherent ambiguity of the fair use doctrine, which is evaluated on a case-by-case basis rather than by strictly-set standards. This flexibility provides power player AI companies room to maneuver around copyright infringement claims, even as authors contend that such practices devalue their creative output and disrupt traditional revenue models.

Subsequent litigation has continued to test the boundaries of legal boundaries regarding AI. In Kadrey v. Meta Platforms, Inc., plaintiffs sought to expand the legal protection claiming that copyright offers to include not just scanning  publicly available copyrighted works but also made a claim against obtaining authors’ work illegally – extracting it from “shadow libraries.” Here, the issue was twofold: not only did it matter how much material was used, but also whether it was obtained from legally dubious sources. Specifically, the case underscored concerns that companies may be reaping financial benefits from data that was not properly licensed, essentially free-riding on the creative labor of authors.

Another significant issue was brought up in Tremblay v. OpenAI, Inc., which discussed self-conflicting claims made in naming the underlying difference on input vs output. The issue notably being that claims brought rely on the AI output accuracy in the context of copyright infringement, when the information scrapped is rather a discussion of input prompts and data sources. Although the plaintiffs accused OpenAI of and named hallucinated data from its training corpus, the defendant logically argued that since hallucinating is essentially derivative of information in itself, no direct copying had occurred. Claiming on copyright infringement yet relying not copying itself but hallucination widened the debate of input vs output distinction and further complicating claims of copyright infringement that judges would be expected to rule on in the future.

Finally, The New York Times v. Microsoft Corp. brought attention to the issue of AI’s memorization of copyrighted content. Here, the NYT argued that AI’s verbatim recitation of journalistic work not only undermines the original effort of its writers but also risks redirecting audiences away from the source extending the claim on Article I Clause 8, which also leads to the argument of remuneration for author’s work. Seemingly, this triggers disruption to market theory.

In all the above cases, American courts have largely refrained from imposing strict injunctions against AI, creating a legal environment that often favors innovation over rigid copyright enforcement. In contrast, Europe has taken a much more restrictive approach, marked by its strict regulatory framework. The EU’s robust data protection laws–epitomized by the General Data Protection Regulation (GDPR)–empower authorities to act decisively against companies that misuse personal data. For instance, Clearview AI’s aggressive scraping of billions of images led Dutch regulators to impose fines of over 30 million euros, which was later compounded by additional penalties in other EU countries. Similarly, the case against X (formerly Twitter) by the Irish Data Protection Commission underscored how ambiguous user consent practices, such as hiding default “opt-out” options behind convoluted settings, can violate fundamental privacy rights.

The EU’s insistence on clear, explicit consent and the ‘right to be forgotten’ create a legal landscape where data processing for AI training must be justified on legitimate interests to have basis to obtain the training data. The strict provisions of various EU Directives make it near-impossible for companies to scrape large amounts of data, which is necessary to create a training corpus for generative AI in the first place – and thus minimizes AI business traction in the EU compared to the US.  Meanwhile, the US currently grants broad freedom to companies with focus on a definition of fair use and minimal intervention. Ultimately, the “genie” of AI in the US may prove to be too powerful to completely confine, whereas the EU does not allow the “genie” to leave the bottle in the first instance.

While AI companies currently face an unregulated, free market in the US, the fines and legal issues that similar companies in the EU deal with reveal potential challenges these US businesses might face in the future. The fines and legal challenges faced by companies operating in the EU reveal a regulatory challenge to be considered when shaping market’s regulatory future in the USA or taken into considerations by AI players when scaling abroad. While hard policies may not be adopted because of the prominence of the innovation economy and the historical freedom that start-ups have had in the US, some form in regulation will have to be achieved in the future and implications might be alike even at lower scale.

From a venture capitalist’s standpoint, early-stage AI startups already carry a heightened exposure to data and copyright litigation, while rapidly-shifting regulatory requirements make it unclear how hurdling the regulation might become. This risk is magnified if business is also conducted in the EU, given that the GDPR raises the stakes for due diligence and compliance. Private equity investors, who typically focus on AI-layered technologies and established companies, face the added burden of ensuring that established revenue streams remain insulated from privacy fines, reputational damage, and operational disruptions caused by potential technological overrides of regulation or intellectual property disputes. Although these challenges may seem minor because AI currently remains unregulated, the legal risk exposure that these technologies expose investors to should be kept in mind as regulations change in the future and business plans should be drafted with outlook on framework from various authorities such as the National Institute of Standards and Technology AI Risk Management Framework or the Federal Trade Commission Guidelines on AI and Automation.

The legal battle between OpenAI and DeepSeek has ignited heated debate about artificial intelligence innovation, intellectual property rights, and competitive dynamics in the AI industry. OpenAI–the undisputed industry leader in generative AI backed by billions in funding–alleges that DeepSeek violated its terms of service by leveraging a technique known as “distillation” to build a competitive product.

Distillation is a method in AI development that enables a smaller “student” model to replicate or approximate the performance of a larger “teacher” model by learning from its outputs. Due to the method’s ability to lower computational costs, distillation has become a widely regarded technique for creating AI systems more efficiently. However, OpenAI claims that DeepSeek’s approach—reportedly querying OpenAI’s model at scale and using its responses as training data to improve DeepSeek’s own AI—crossed a line by extracting OpenAI’s proprietary data without permission.

If OpenAI’s allegations lead to a legal battle, the outcome could have a substantial impact on the future of the generative AI industry. This case highlights a recurring dilemma in the tech industry: how to balance the right innovators have to protect their technological advancements against the broader public interest in ensuring open access to transformative technologies. A notable example of this was the legal dispute between Oracle and Google over the use of Java APIs in the Android operating system. Oracle sued Google, alleging that functional software elements (such as APIs) are subject to copyright protection; thus, the replication of Java APIs on Android constituted copyright infringement. In 2021, the U.S. Supreme Court ruled in favor of Google, determining that Google’s use of the Java APIs was a lawful fair use. The Court emphasized that certain forms of software replication can drive innovation rather than hinder it, outweighing Oracle’s desire to control its copyrighted software.Had Oracle prevailed, developers might have faced significant restrictions on API usage, potentially stifling interoperability and innovation within software ecosystems.

A similar dynamic is playing out in the AI industry. OpenAI’s massive financial and research investments, including a recent $6.6 billion funding round, have driven the development of advanced models like GPT-4.5. A ruling in favor of OpenAI would strengthen the legal protection that companies with AI models have, preventing other companies from using distillation. This would provide firms with greater legal assurance that their technological advances cannot be easily reproduced, encouraging further investment in AI research. Companies with large-scale computational infrastructure, which would benefit from a more predictable and enforceable intellectual property landscape, would likely be incentivized to expand their AI initiatives.

Additionally, such a ruling could pave the way for industry-wide licensing frameworks, where AI firms must obtain explicit permissions or pay for access to use large proprietary models rather than extracting their outputs through distillation. This shift could lead to a more standardized business model in AI, allowing companies to commercialize access to their models while ensuring that smaller firms can still legally participate through licensing agreements. As a result, AI developers could receive financial compensation for their innovations, creating a sustainable ecosystem where AI research is funded without fear of imitation.

Conversely, if distillation is considered a legitimate practice, it would make the development of advanced AI accessible to a wider range of companies. This would drastically change the dynamics of the AI industry. Currently, building state-of-the-art AI models requires enormous computational resources, access to vast datasets, and significant financial backing. These barriers make it nearly impossible for smaller startups or independent researchers to compete with tech giants like OpenAI, Google DeepMind, and Anthropic, which have billions of dollars in funding and access to specialized hardware such as high-end GPUs and TPUs.

By allowing distillation as a legal practice, smaller AI companies could train efficient models using knowledge extracted from larger, more advanced AI systems without having to replicate the expensive training process from scratch. Instead of needing to collect and process massive datasets—often a key advantage held by large companies—smaller firms could leverage the distilled knowledge from publicly available models or even commercial APIs to develop lighter, more cost-effective AI systems tailored to specific use cases.

For example, a healthcare AI startup that lacks the resources to train a large-scale medical language model from the ground up could apply distillation techniques to a commercially available model to develop a specialized AI assistant for doctors. Similarly, a legal tech firm might use distillation to fine-tune an AI system focused exclusively on contract analysis, making legal AI tools more affordable and widely available to law firms and in-house legal teams.

Distillation could thus encourage AI adoption in industries that large tech companies typically overlook. While OpenAI and Google DeepMind focus on broad, general-purpose AI systems, smaller companies could use distilled models to create highly specialized AI solutions for niche markets such as agriculture, local governance, small business automation, and environmental monitoring. These applications might not be financially viable for large tech firms to pursue but could thrive in a more open AI ecosystem where smaller players have access to efficient AI development techniques.

This increased accessibility would create a more competitive AI landscape, as more companies could afford to develop their own models without relying on a handful of dominant firms for licensing access. Instead of AI advancements being controlled by a few large corporations, startups and independent developers would have more opportunities to innovate, leading to faster technological progress and more diverse AI applications across different industries.

Ultimately, if distillation remains a widely accepted practice, it would democratize AI innovation, making it more feasible for smaller players to enter the industry and compete with established giants. This shift would not only increase competition and drive down costs for AI-powered products and services, it would also lead to a more diverse and inclusive AI landscape. Innovation would not solely be driven by a few massive corporations but rather by a global network of researchers, startups, and independent developers working on AI applications that address a wide range of real-world challenges.

The ongoing debate surrounding the potential legal dispute between OpenAI and DeepSeek encapsulates a pivotal moment in the evolution of artificial intelligence. This situation compels stakeholders to weigh the benefits of democratizing technology against the necessity of protecting significant investments and maintaining industry standards. As discussions progress, the implications are likely to ripple through the AI industry, potentially reshaping the landscape of innovation, market dynamics, and regulatory policies for years to come.