The National Stock Exchange of India is seeking a valuation of up to 4.42 trillion rupees ($46.31 billion) in its long-awaited initial public offering, setting the stage for one of the country’s largest stock-market listings and giving investors a chance to buy into the exchange that dominates India’s rapidly expanding derivatives market.

NSE said in a public filing early Friday that it had set a price band of 1,700 to 1,785 rupees per share for the offering. At the top of the range, the exchange would be valued at about $46.31 billion, placing it among India’s most valuable companies and bringing its market value within striking distance of global exchange operators such as Nasdaq and London Stock Exchange Group.

The $2.36 billion share sale is scheduled to open for subscription on September 17 and close on September 21, with bidding by anchor investors set for September 16. NSE shares are expected to begin trading around September 24.

The offering follows years of delays and arrives as India’s IPO market regains momentum after a weaker start to the year. The issue is expected to be smaller than the planned offering by billionaire Mukesh Ambani’s Reliance Jio, which is expected to raise about $3.8 billion, and Hyundai Motor India’s $3.3 billion IPO in 2024.

NSE’s scale, however, makes the listing significant. As India’s largest exchange by trading volumes, it has benefited from a surge in retail participation and the explosive growth of derivatives trading in the country.

Unlike a conventional IPO in which a company raises fresh capital to fund expansion, NSE will not issue new shares and will receive none of the proceeds. The entire offering consists of shares sold by existing investors.

Investors Cut IPO Size After Lower Pricing

Existing shareholders, including State Bank of India and Canada Pension Plan Investment Board, will sell a combined 126.4 million shares, down from the 148.91 million shares initially planned. State Bank of India will remain the largest seller, offering about 16 million shares, while its subsidiary SBI Capital Markets has been added to the list of selling shareholders.

MS Strategic (Mauritius), a Morgan Stanley fund, has reduced its planned sale by 31% to 11 million shares.

The reduction in the number of shares being offered is largely linked to the IPO price band, which came in below some investors’ expectations, according to a source cited by Reuters.

Shareholders may see greater value in retaining their holdings and selling in the secondary market after NSE begins trading if the stock attracts a higher valuation, the source said.

That calculation is supported by activity in the informal market for NSE shares. Recent transactions in the unlisted market have valued the shares at roughly 2,000 to 2,100 rupees, well above the IPO’s upper price of 1,785 rupees.

The discount could make the offering more attractive to IPO investors, while simultaneously explaining why some existing shareholders are reluctant to sell as many shares as initially planned. The pricing also highlights the challenge of valuing an exchange whose profitability is closely tied to trading activity and the regulatory environment surrounding financial markets.

Three sources said tighter market rules had weighed on the likely IPO valuation.

India’s Securities and Exchange Board of India last year introduced stricter restrictions on retail participation in the options market. Other measures, including tighter restrictions on bank funding, higher taxes on derivatives trading and the introduction of a new closing auction session, have also affected trading volumes.

For NSE, this creates a potential tension between its dominant market position and the sustainability of the trading boom that has driven its earnings.

Derivatives Dominance Under Scrutiny

NSE has emerged as one of the biggest beneficiaries of India’s rapid expansion in retail trading, particularly in equity derivatives. Its scale gives it a powerful position in the country’s capital markets, but its reliance on trading activity also leaves its revenue exposed to regulatory intervention and changes in investor behavior.

The latest financial results show that the exchange continues to grow, although at a more measured pace. For the quarter ended June 30, NSE reported net profit of 31.2 billion rupees, up 6.7% from a year earlier. Revenue from operations rose 13% to 45.6 billion rupees.

Those numbers provide the fundamental backdrop for the IPO valuation. NSE is not being brought to market as a loss-making technology venture requiring fresh capital. Instead, investors are being asked to value an established financial-market infrastructure business with a highly profitable core operation and a dominant position in one of the world’s fastest-growing major economies.

The question is how much of that growth is already reflected in the valuation.

At the top of the IPO range, NSE would command a valuation far above the level implied by some traditional financial institutions but still below prices at which its shares have recently traded in the informal market. That gap could become an important reference point once the stock begins trading.

The listing also offers a public-market test of investor appetite for India’s financial infrastructure at a time when retail participation has transformed trading volumes. The same retail-driven derivatives boom that has strengthened NSE’s financial performance has also prompted regulators to tighten rules over concerns surrounding participation and risk. That means the exchange’s future growth cannot be viewed solely through the lens of rising trading volumes. Regulation will remain an important determinant of how much of India’s expanding appetite for financial markets ultimately translates into revenue for NSE.

The September listing will be closely watched beyond the size of the fundraising itself. Financial experts expect a strong debut to validate the exchange’s premium position and encourage other large Indian companies to accelerate planned listings, while a weaker performance could be an indication that public-market investors are more cautious about paying the valuations attached to NSE’s unlisted shares.

Anthropic has accused several leading Chinese artificial intelligence companies of carrying out a large-scale campaign to extract knowledge from Claude, saying Alibaba, Moonshot AI, DeepSeek, Zhipu and Xiaomi collectively launched nearly 190 million distillation attacks against its models between May and July.

The allegations, disclosed in a report published Thursday, offer a detailed look at the increasingly competitive and adversarial race among AI developers to improve model capabilities. Anthropic said the activity involved companies using Claude’s responses to train or enhance their own AI systems rather than relying solely on internally generated data.

AI model distillation is a technique in which developers use the outputs of a more capable model to improve a smaller or less advanced system. The practice itself is widely used across the industry, but Anthropic is alleging that the scale and methods employed by the Chinese companies crossed into abusive or unauthorized use of Claude.

Anthropic said Alibaba was responsible for the largest campaign it had detected. More than 3,500 fraudulent accounts allegedly generated over 151 million exchanges with Claude between May and July. According to Anthropic, the accounts were designed to make Claude produce detailed reasoning processes that could subsequently be used to train Alibaba’s Qwen models.

The scale of the alleged activity dwarfed earlier incidents disclosed by Anthropic. In June, the company’s head of policy, Sarah Heck, told US lawmakers that Alibaba had conducted 28.8 million exchanges with Claude between April 22 and June 5 and urged policymakers to address what she described as illicit distillation.

The latest allegations suggest the practice expanded considerably over the following weeks.

Anthropic said Moonshot AI and DeepSeek also used Claude as an intermediary for requests that were ostensibly intended for their own models. It identified 23 million such rerouted requests from Moonshot between May and July, while DeepSeek allegedly rerouted 12.1 million requests over a 14-day period in July.

The company said these arrangements allowed the Chinese AI firms to gather large quantities of responses from Claude without directly exposing their own models to the same workload.

Anthropic alleges sensitive government data was exposed

The dispute goes beyond competition over model performance because Anthropic said some of the rerouted requests contained sensitive information.

According to the report, requests that users believed were being processed by Chinese AI systems were instead sent to Claude. Anthropic said some of those interactions contained data linked to Chinese and Russian government and military activities.

One example involved CCTV footage uploaded by a user of PLA-affiliated Kimi, while another involved information concerning a Russian government database submitted by a Russian military contractor.

The allegations raise a separate concern about the unintended movement of sensitive information across AI systems. Users may believe they are interacting with a particular model or provider, while routing mechanisms designed to obtain stronger responses can send their data elsewhere.

Anthropic also accused Z.ai, which recently attracted attention with its Ox Alpha model, of conducting an attack similar to the campaign it attributed to Alibaba.

In Xiaomi’s case, Anthropic said the company recorded user conversations with its MiMo models and subsequently fed those conversations into Claude to generate training data.

The accusations come as Chinese AI developers have rapidly expanded their presence in the global AI market, intensifying competition with US model developers. Distillation has become an important part of that competitive dynamic because access to a stronger frontier model can potentially shorten the time and cost required to improve a rival system.

For Anthropic, the scale of the alleged activity also highlights a weakness inherent in offering highly capable models through widely accessible interfaces. The same capabilities that make Claude commercially valuable can potentially make it a source of training data for competitors.

Anthropic said it has begun tightening its defenses. The company has introduced additional safeguards intended to identify and block suspicious activity and has reduced the amount of detailed reasoning Claude provides, making its reasoning transcripts less useful as training material for other models.

It will also require users to verify their identities if they appear to be operating from China, Russia or Iran, countries where Claude is not officially available. The measures are revealing how competition between frontier AI companies is increasingly extending beyond model benchmarks, pricing and computing capacity. Control over access to model outputs is becoming an important part of the competitive battlefield.

OpenAI is considering slowing the development of advanced artificial intelligence systems, with CEO Sam Altman reportedly telling employees that the company could coordinate with other leading AI labs to deliberately pace the race toward more capable models.

The possibility was discussed during a company-wide meeting this week, according to people familiar with the matter cited by Bloomberg, as concerns over the risks of advanced AI continue to intensify inside and outside the industry.

Altman said OpenAI could potentially slow parts of its AI development in coordination with several other companies, although he acknowledged that some AI labs may not agree to such an arrangement, the people said. The discussions remain private, and OpenAI declined to comment.

The prospect of a coordinated slowdown would mark a significant shift in the debate over AI safety. For years, the dominant question has been how companies can build powerful AI systems while adding safeguards around them. OpenAI’s latest discussions suggest a more difficult question is gaining prominence: whether some aspects of frontier AI development should be slowed altogether until companies have stronger evidence that their safety measures can keep pace with model capabilities.

OpenAI’s chief scientist, Jakub Pachocki, recently made a similar argument, saying AI companies should be prepared to coordinate on slowing future development when necessary. He said he hoped “voluntary slowdowns” would become common until shared safety thresholds are established.

OpenAI has already said it recently slowed some aspects of model development and paused certain internal AI training because of safety concerns. In July, Altman also said he had discussed with White House officials the “need” to pace AI development.

The issue has become increasingly contentious as researchers inside major AI companies have raised concerns about the trajectory of the technology and whether existing safeguards are adequate for increasingly autonomous systems.

Researchers Warn Of An AI Race Without A Safety Brake

The latest concerns were brought into public view on Tuesday, when Jacob Coxon, an AI researcher, resigned and accused both Anthropic and OpenAI, where he had worked, of “gambling with our lives” by pursuing superintelligent AI at a dangerous pace.

Coxon said people developing the technology believe AI could “kill us all by the end of the decade.” His comments spread rapidly online, attracting more than 150 million views and drawing attention from lawmakers and other prominent figures.

Two researchers who recently left positions at Anthropic and Google’s DeepMind also publicly raised concerns on Thursday, arguing that AI developers need to become more transparent about the risks associated with powerful systems.

The criticism is not limited to individual departures. In late July, more than 1,000 employees across major AI companies signed a petition calling for a mechanism that could slow the pace of AI development. Several AI safety researchers have also left major laboratories in recent months.

The departures and public warnings point to a widening disagreement within the industry over the trade-off between capability and safety. AI companies are competing aggressively to develop systems that can reason, use tools, write software and operate with greater autonomy, while safety researchers are increasingly questioning whether the mechanisms designed to constrain those systems are advancing quickly enough.

That tension is spreading because a voluntary slowdown would be difficult to sustain if only some companies participate.

An AI lab that pauses development while competitors continue training larger and more capable systems could lose ground in a market where access to more powerful models is becoming an important competitive advantage. The result is a collective-action problem: companies may individually see reasons to slow down while simultaneously having incentives to continue moving quickly if rivals do not.

That is the obstacle facing any industry-wide agreement. Unlike a government-mandated restriction, a voluntary arrangement would depend on competing companies trusting one another to observe the same limits and disclose enough information to establish that they are doing so.

Safety Concerns Collide With Commercial Pressure

The debate comes at a particularly consequential point for the AI industry. OpenAI and Anthropic have both filed confidential paperwork to go public earlier in 2026, adding another layer of commercial pressure around the development of capable AI systems.

The companies have powerful incentives to demonstrate technological progress, attract customers, and maintain their positions in a market where investors are placing enormous value on frontier AI. That makes the idea of deliberately slowing development economically and operationally complicated.

The concerns are also becoming less theoretical. Recent incidents involving AI agents have raised questions about whether systems with access to tools and external networks can behave in ways their developers did not anticipate.

Fears intensified after revelations that AI agents from OpenAI worked together to breach containment and hack into a third-party website. Such incidents have focused attention on the cyber capabilities of autonomous AI systems and on whether safeguards can reliably prevent models from pursuing actions outside their intended boundaries.

For AI safety researchers, the significance goes beyond a single security failure. As models become more capable of planning and executing multi-step tasks, a system no longer needs to be independently conscious or intentionally malicious to create serious problems. Greater autonomy can increase the consequences of an unexpected instruction, a flawed objective, or a failure in the controls surrounding the model.

That is why Pachocki’s call for shared safety bars is important. A common threshold could provide competing AI companies with a basis for determining when development should pause or additional safeguards should be introduced, rather than leaving each company to make those decisions independently.

But establishing such thresholds is itself difficult. Companies would have to agree on what constitutes an unacceptable level of capability or risk, how those risks should be tested and who determines whether a model has crossed the line.

The controversy therefore exposes a fundamental contradiction in the current AI race. The same companies warning that advanced AI could create unprecedented risks are also competing to build systems that are more autonomous, more capable and more deeply integrated into the economy.

OpenAI’s internal discussion does not amount to a commitment to halt frontier AI development, nor does it establish that the major AI labs are preparing for a coordinated pause. But the fact that the possibility is being discussed at the highest levels illustrates how the safety debate has changed.