AP & Partners

Authored by – Moksha Bhat, Managing Partner at AP & Partners, And co-authored by – Udit Kapoor, Associate, AP & Partner Introduction Over the past decade, India has become a major start-up hub and now has the third largest number of unicorns—companies valued over USD 1 billion. This growth has been spurred in large measure by foreign capital, particularly venture capital and private equity investors. To access this capital, many Indian start-ups adopted a “flip” structure—incorporating offshore holding companies (commonly in jurisdictions like the US or Singapore) to facilitate fundraising, align with investor preferences, and enable listings on global exchanges such as NASDAQ.  These structures typically involve a non-operating foreign holding company owning a wholly owned Indian subsidiary that houses the operational business. However, this trend is now reversing. Many Indian-origin start-ups are now “reverse flipping” back to India—restructuring so that investors and founders hold shares directly in the Indian company. The primary drivers include stronger domestic capital markets, deepening pools of domestic risk capital, and an increasing number of successful Indian IPOs. Reverse flips – considerations The optimal structure for a reverse flip depends on multiple factors, including tax efficiency, deal timeline, regulatory complexity, and the jurisdictions involved. Common approaches include: Inbound Mergers: a foreign holding company merges with an Indian company, with the Indian entity surviving. Share Swaps/Exchanges: Shareholders of the offshore company directly acquire shares in the Indian company in exchange for their existing holdings. While inbound mergers can be structured to be tax-neutral under Indian law, they can be time-consuming (taking up to a year), unless the fast track route is available. Share swaps may be faster but could trigger capital gains tax depending on treaty relief availability and valuation differentials. Mergers In India, an inbound merger of a foreign company with an Indian company is governed by the provisions of: The (Indian) Companies Act, 2013 (“Companies Act”) and the Companies (Compromises, Arrangements and Amalgamations) Rules, 2016; and The (Indian) Foreign Exchange Management Act, 1999 (“FEMA”) and the rules framed under it, mainly the Foreign Exchange Management (Cross-Border Merger) Regulations, 2018 (“FEMA Merger Regulations”). In brief, the process in India to implement a merger can either require the approval of (a) National Company Law Tribunal (“NCLT Route”), a specialised tribunal set up under the Companies Act for issues relating to Indian companies, or (b) the Central Government of India through Regional Directors (“Fast Track Route”). NCLT Route The merger through NCLT Route is usually a more drawn-out process, involving the following steps: The parties to the merger approach NCLT with a “scheme of arrangement” which sets out the manner in which the reorganisation would be implemented. NCLT calls meetings of shareholders and creditors to approve the scheme with the prescribed voting thresholds. These meetings can be waived if written consents are obtained from the prescribed number of shareholders and creditors. The scheme is then notified to various government authorities and to the public through a public notice process. NCLT approves the final merger order after resolving objections (if any), and the order is filed with the Registrar of Companies. A foreign company may merge with an Indian company after both obtain approval from the Reserve Bank of India (“RBI”). Some mergers may qualify under the deemed approval framework (discussed below). Fast Track Route For certain eligible companies, the Fast Track Route is also available where the merger scheme is considered and approved by the Central Government without the need to approach the NCLT. This Fast Track Route has relatively lower compliance requirements and can be undertaken in a shorter time frame. The Fast Track Route can be used for the inbound merger of a foreign company with an Indian company provided that the Indian company is a wholly owned subsidiary of the foreign company. Navigating Indian capital controls Indian exchange control regulations add an additional layer of complexity to be navigated for such reverse flip transactions. As background, the FEMA sets out the framework for foreign investment into India. This includes matters such as pricing guidelines that apply to such transactions, sectoral caps, investment conditions, and reporting requirements. Cross-border mergers transactions are viewed as capital account transactions under the FEMA. Such transactions require prior approval of the RBI unless specifically permitted under the FEMA or the regulations framed under it. Under the FEMA Merger Regulations, cross-border transactions are categorised as either falling under the automatic route, that is, transactions that can be undertaken without the approval of the RBI, or under the approval route, that is, transactions that require prior approval of the RBI. Inbound mergers of foreign companies with Indian companies are deemed to be approved by the RBI subject to certain specified conditions including: Issue or transfer of securities by the resultant Indian company to non-residents must comply with FEMA provisions, including sectoral caps, pricing guidelines, entry routes, and reporting requirements. Off-shore borrowings and guarantees taken over by the Indian company must be brought in with FEMA regulations within two years; no repayment remittance is allowed during this period. The Indian company may acquire, hold, and transfer overseas assets per FEMA. If not permitted, such assets must be sold within two years of NCLT sanction. A foreign currency bank account can be opened for incidental transactions related to the merger, valid for two years post-NCLT approval. All FEMA-related non-compliances or violations prior to the merger must be resolved. Valuation of the foreign company must be done by recognised valuers in the relevant jurisdiction, following internationally accepted principles. If a merger does not comply with the above conditions, an RBI approval would be required for such a merger. Other considerations There are other issues that need to be evaluated when considering a reverse flip transaction, including: Presence of investor from certain jurisdictions: If an investor or beneficial owner is based in a country that shares a land border with India such as China, RBI approval is required. This must be reviewed before finalising a reverse flip. Issues under listing regulations: If the reverse flip is aimed at an IPO in India, listing regulations like minimum shareholding periods, valuations, and disclosure requirements must be checked in advance. Sectoral approvals: Businesses in regulated sectors like financial services may need additional regulatory approvals or need to inform authorities due to a change in ownership or control after the merger. Conclusion The recent surge in reverse flips underscores greater availability of risk capital and the growing maturity of Indian capital markets. In response, Indian regulators have taken steps to streamline inbound merger processes. However, this remains a relatively new and evolving area. The government should look to encourage this trend and evolve a single window clearance framework to make it easier to re-domicile companies to India. At the same time, founders and investors should carefully evaluate legal, tax, regulatory, and commercial considerations before proceeding with any reverse flip transaction.

Authored by Lagna Panda, partner AP & Partners AI-related technologies and products are evolving more rapidly than one can imagine. The hype around generative AI (GenAI)¾which was quite short-lived¾is now giving way to agentic AI. The developments in the AI industry have attracted interest and intrigue of antitrust regulators. A few antitrust regulators have initiated (and, in some cases, completed) market studies to identify potential competition concerns in AI markets. This article analyses some of these concerns including algorithmic collusion, access to compute, and AI partnerships. One of the initial concerns regarding AI that cropped up was ‘algorithmic collusion’: in markets where prices change frequently (perhaps, even multiple times in a day), competitors can use the same software to engage in price-fixing conduct. This hypothesis might be an oversimplification of the agentic nature of AI and how external-facing pricing mechanisms work. That said, without an ‘agreement’ or ‘understanding’ to not compete, competitors have strong commercial incentive to lower prices to complete a sale instead of maintaining price parity. For instance, an online retailer may employ an AI-based tool to track the prices of its competitors on a real-time basis and offer the same prices. However, it will have the commercial incentive to offer lower prices to achieve higher sales. There are also concerns around entry barriers in relation to inputs such as data and compute, for building large language models (LLMs) and foundational models (FMs). Before delving into the specifics of key inputs, it is important to acknowledge that we are still at the very cusp of the AI revolution. Capital allocation (internal and external) towards AI has been significant. Not only are the Big Tech players seriously investing in the AI space, but start-ups working in different areas of the AI stack have fairly easy access to capital. Data: Datasets used to train LLMs and FMs can be public data without copyright protection, public data with copyright protection, non-public copyrighted content, government data, synthetic data, proprietary datasets, and specialized datasets. As the use of publicly available data without copyright protection becomes saturated, demand for other categories of datasets such as synthetic data and public data with copyright protection will increase. We are already beginning to see this. Amazon has entered into a copyright licensing agreement with the New York Times. OpenAI has struck a similar deal with Condé Nast. Licensing deals are seeing an uptick as there is legal ambiguity around use of copyrighted materials to train AI models. Aside from copyright infringement claims in various jurisdictions, we are also seeing launch of tools like Cloudflare’s ‘pay-per-crawl’ tool, to prevent free scraping of copyrighted content. At this stage, where the use of different categories of data to train AI models is being contested or restricted, and use cases are still being explored, it will be premature to conclude that access to data obtained through specifics apps or services like a social media app or a messaging service can act as an entry barrier. Compute: The demand for chips, particularly GPUs, has increased dramatically given GPUs’ suitability for training and fine-tuning generative AI (GenAI) models. While Nvidia has been a major GPU supplier, Big Tech firms have begun investing heavily in developing their own chips because of the pace of AI advancement. Meta is developing its own AI training chips – Meta Training and Inference Accelerator (MTIA). Google has deployed tensor processing units (TPUs) which are being used to run Google’s AI services. Amazon Web Services is using custom Trainium, Graviton and Inferentia chips for AI workloads. Microsoft has deployed Maia chips and is developing Braga chips to meet its AI infrastructure needs. Then there are new-age semiconductor startups in the USA that are developing AI chip architecture like Groq and Cerebras Systems. Other countries are also witnessing immense innovation in this space: Huawei Technologies (China) has launched its series of Ascend AI chips, Rebellions (South Korea) is developing AI chips that use high bandwidth memory, and FuriosaAI (South Korea) is working on designing ‘RNGD’ AI chips. Given the extent of innovation in the AI infrastructure stack and the evolving nature of AI use cases, competition concerns in any market relating to AI compute inputs, seems unlikely. In new, evolving markets, firms may enter into agreements to supply or purchase components and services to generate efficiencies. While we are seeing quite a few partnerships in the AI space (e.g., Perplexity offering access to Perplexity Pro for free for one year to Airtel users), given the high dynamism prevalent, it appears unlikely that any partnership has the ability to impact competitiveness of any AI market in India. Having said that, it will be interesting to see how the Competition Commission of India views AI-related partnerships from a merger control standpoint given that the threshold for ‘control’ is set at ‘material influence’. AI continues to rapidly evolve and its impact across industries and sectors is expected to be nothing short of ground-breaking. While conducting market studies can be a very productive exercise to gauge and understand how market dynamics are shaping up, any regulatory intervention at an early stage can lead to unintended consequences and do more harm than good. For the time being, regulators may take a wait-and-watch approach and let the chips fall where they may.