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The Tokyo-listed investment group has become one of the most closely watched proxies for AI demand after committing tens of billions of dollars to OpenAI and building exposure across chips, power infrastructure, robotics and data-centre supply chains. The latest rally extended a sharp two-session advance that added tens of billions of dollars to SoftBank’s market value, marking one of its strongest runs since the technology boom that first made the group a symbol of risk-taking capital.

Investor enthusiasm has been driven by two linked expectations. OpenAI is preparing for a confidential US initial public offering filing that could set up a listing as early as September, while SB Energy, a power and data-centre infrastructure company backed by SoftBank and OpenAI, has said it intends to confidentially file for a US IPO. Together, the developments have strengthened the market view that SoftBank’s private holdings may be moving closer to public-market price discovery.

SoftBank’s OpenAI position is the main driver of that reassessment. The group has invested $34.6bn in the ChatGPT maker since September 2024 and has agreed to make a further $30bn investment through SoftBank Vision Fund 2. Completion of those tranches would lift its cumulative investment to $64.6bn for an ownership interest of about 13 per cent. The preferred shares are designed to convert automatically into common shares if OpenAI completes an IPO or related listing transaction.

OpenAI’s valuation has become central to SoftBank’s investment case. The company was valued at $852bn in private markets and preliminary discussions around a listing have included the possibility of a valuation as high as $1tn. The company is also seeking to raise at least $60bn at the lower end of earlier discussions, reflecting the huge capital needs of frontier AI model development, data centres, chips and power supply.

The OpenAI bet has already reshaped SoftBank’s earnings profile. The group reported a sharp profit rebound, helped by valuation gains tied to OpenAI, and said cumulative gains on the investment had reached $45bn. Its annual profit of about ¥5tn was described by the company as the highest recorded by a Japanese company, underscoring how quickly Son’s AI strategy has altered investor perceptions after years of scrutiny over the Vision Fund’s uneven performance.

SB Energy has added another layer to the rally because its business sits at the intersection of AI and power infrastructure. Founded in 2019, the company develops large-scale energy and data-centre projects and has positioned itself to benefit from surging electricity demand linked to AI workloads. It has raised more than $18bn in project capital and has a portfolio of about 5 gigawatts of operating and under-construction energy assets.

The company’s January partnership with OpenAI under the Stargate initiative placed it inside a broader push to build AI and energy infrastructure in the United States. That has made SB Energy more than a conventional renewable power developer in the eyes of investors. Its appeal rests on the argument that AI expansion will require dedicated power generation, grid connections and purpose-built campuses capable of supporting high-density computing.

SoftBank’s rally has also been supported by strength in Arm Holdings, the UK chip designer in which the group holds a large stake. Arm remains a key public asset for SoftBank and is viewed as a beneficiary of AI-related semiconductor demand. Gains in Arm shares have helped reinforce confidence that SoftBank owns multiple assets tied to the same structural theme, rather than a single concentrated wager on OpenAI.

The optimism carries risks. OpenAI faces intensifying competition from Alphabet’s Gemini, Anthropic’s Claude and other model developers, while the cost of training and operating advanced AI systems continues to rise. Credit analysts have raised concerns that the size of SoftBank’s OpenAI commitment may affect liquidity and asset quality, particularly as the group uses loans, asset sales and financing backed by holdings such as Arm and SoftBank Corp. to fund its strategy.

SoftBank arranged a $40bn bridge loan in March and drew $20bn in April, largely for the OpenAI investment, while also repaying part of that borrowing. The group has sold stakes in holdings including Nvidia and T-Mobile, issued bonds and considered other financing options as it reallocates capital towards AI. Chief Financial Officer Yoshimitsu Goto has said the group may also explore ways to use its OpenAI assets for financing if market conditions allow.

A 2026 software supply chain security assessment shows that about 65 per cent of organisations in India cannot detect malicious packages, while 71 per cent do not use container security tools. The findings underline a widening gap between the speed of AI-led software development and the controls needed to secure code, dependencies, models and deployment pipelines before they reach production systems.

Security teams are facing a more complex threat environment as AI coding assistants, model registries, agent frameworks and machine-learning libraries become embedded in day-to-day software engineering. AI is no longer confined to application features or analytics layers. It is becoming part of the software supply chain itself, influencing how code is written, tested, packaged and deployed.

Enterprises in India have been quick to adopt AI tools to improve developer productivity, reduce repetitive work and accelerate product releases. Yet the same shift has increased reliance on open-source packages, pre-trained models, plug-ins and automated build systems. Each of these components can introduce hidden risks if provenance, integrity and behaviour are not continuously verified.

Global software repositories are now attracting sustained attacks because they provide a direct route into enterprise development environments. Malicious npm packages rose by 451 per cent during 2025, while more than 48,000 new Common Vulnerabilities and Exposures were disclosed worldwide, a rise of about 20 per cent from the previous year.

The pressure on security teams is being amplified by the volume of AI-generated code. Developers in India are spending about 51 per cent of their time reviewing, validating and hardening code produced by AI tools. About 53 per cent treat such output only as a starting point and review every line before use, while 11 per cent rewrite AI-generated fixes from scratch.

This shift has altered the economics of software engineering. AI may reduce the time needed to draft code, but it can also transfer effort to verification, testing and security review. Weaknesses such as cross-site scripting, SQL injection and missing authorisation checks can still be reproduced by automated tools, particularly when developers rely on generated code without adequate scrutiny.

Governance is emerging as another area of concern. Nearly 97 per cent of organisations surveyed said they had certified AI model governance programmes, but only 59 per cent claimed full provenance visibility across production environments. About 48 per cent still needed a week or longer to produce audit-ready compliance evidence.

That gap points to a difference between policy and enforceable control. Many enterprises have AI governance frameworks on paper, but fewer can prove where every model, library, container image and software artefact came from, how it changed, who approved it and whether it complies with internal policy.

CERT-In has also warned that software supply chain compromises are increasingly targeting developer tools and trusted repositories. Its April 2026 activity note referred to attacks affecting npm, PyPI, GitHub Actions and container registries, including incidents involving Checkmarx, Trivy, LiteLLM, Axios, Telnyx and npm packages linked to the CanisterWorm campaign. The payloads observed in these attacks included credential harvesting, data exfiltration, remote execution and persistence within developer systems.

Model registries have added another layer to the risk. Around 1.4 million new AI artefacts were published on Hugging Face during 2025, representing 58 per cent of all new software packages tracked in one assessment. Researchers identified 495 malicious AI models in public repositories with active payloads capable of credential theft, command execution and reverse-shell activity, along with 969 malicious AI-agent skills designed to exploit developer environments and automation workflows.

For enterprises in India, the challenge is sharpened by the country’s role as a major software engineering and global capability centre hub. Bengaluru, Hyderabad, Pune, Chennai and Gurugram host large engineering teams serving banking, retail, telecoms, manufacturing and cloud services. These centres are increasingly responsible not only for maintenance work but also for product development, AI integration and security-sensitive digital platforms.

Regulatory and customer expectations are also tightening. Buyers are asking for software bills of materials, vulnerability disclosure processes, provenance records and proof that build pipelines are protected. CERT-In’s technical guidance on SBOM, QBOM, CBOM, AIBOM and HBOM has emphasised regular updates, vulnerability exploitability exchange documents and staff training around software supply chain visibility.

Security leaders are therefore moving towards stronger controls across development pipelines. These include automated malicious package detection, container image scanning, secrets detection, signed artefacts, dependency pinning, model provenance checks, policy enforcement in CI/CD systems and continuous monitoring of developer environments.

The investment case is becoming harder to ignore. Software supply chain attacks can bypass perimeter defences because they enter through trusted development channels. Once inside, malicious packages can steal credentials, alter builds, move into cloud environments or compromise downstream customers. AI raises the stakes because it can accelerate both legitimate development and attacker experimentation.

The New York-founded company, led by Shayne Coplan, is seeking to open discussions around whether event-based contracts can be permitted under Japan’s financial and digital-asset framework rather than treated purely as gambling. The move comes as prediction markets draw heavier institutional interest, stronger trading volumes and sharper scrutiny from regulators over consumer protection, market integrity and the legal boundary between betting and derivatives.

People familiar with the matter said the company’s Japan strategy is at an early stage and would require engagement with officials, lawmakers and industry participants before any formal market entry. Japan does not have a dedicated framework for cash-based prediction markets, leaving platforms that allow users to trade on political, economic, sports or cultural outcomes exposed to uncertainty under gambling and financial laws.

Polymarket allows users to buy and sell contracts tied to the outcome of real-world events, with prices reflecting market-implied probabilities. Its markets have covered elections, central bank decisions, geopolitical events, sports, entertainment and cryptocurrency milestones. The platform gained global visibility during the 2024 US presidential election cycle, when trading volumes surged and its odds were widely cited as an alternative gauge of political sentiment.

Japan would offer Polymarket a large, sophisticated retail market with deep participation in equities, foreign exchange and digital assets. The country has also built one of the world’s more structured crypto regulatory regimes, requiring registration for crypto-asset exchange service providers and imposing rules on custody, anti-money laundering controls and customer protection. That framework may give the company a potential route for engagement, although prediction markets remain a separate and more politically sensitive category.

The company’s global push has accelerated after a period of regulatory pressure in the United States. Polymarket settled a case with the Commodity Futures Trading Commission in 2022 after being accused of offering off-exchange event-based binary options without proper registration. It blocked US users for several years before moving to rebuild a domestic regulatory path through acquisitions and licensing efforts. Its purchase of a derivatives exchange and clearinghouse gave it a route to pursue compliant US operations.

Institutional backing has also strengthened its position. Intercontinental Exchange, the parent of the New York Stock Exchange, agreed to invest up to $2 billion in Polymarket in a deal that valued the business at about $8 billion before the transaction. The partnership gave Polymarket a powerful market infrastructure ally and signalled that event-driven data may have value beyond retail speculation, particularly for investors seeking real-time sentiment around political, economic and corporate developments.

Competition is intensifying. Kalshi, a regulated US prediction market operator, has expanded rapidly by framing event contracts as financial instruments under commodities law. Its growth has triggered legal disputes with state regulators, some of whom argue that sports and election-linked contracts resemble gambling products. That debate is likely to influence Japan’s assessment of whether prediction markets can be supervised as finance, gaming, information markets or a hybrid category.

Polymarket’s Japan plan faces several obstacles. Gambling is tightly restricted, with permitted activity largely confined to state-authorised sectors such as horse racing, bicycle racing, motorboat racing, lotteries and pachinko-linked structures. Online betting carries heightened enforcement risks, and any product involving wagers on uncertain outcomes would need careful legal classification. A crypto-funded model could add further complexity because regulators would need to examine custody, settlement, identity checks, sanctions screening and investor suitability.

Supporters of prediction markets argue that they create useful public signals by aggregating dispersed information into tradable prices. Traders who believe a market is mispriced can buy or sell contracts, pushing prices closer to collective expectations. That model has appealed to political analysts, hedge funds, economists and technology investors seeking faster indicators than polls, surveys or conventional research.

Critics counter that thin liquidity, anonymous participation, insider access and coordinated trading can distort outcomes. Questions have intensified around markets linked to sensitive events, where participants may possess non-public information or where the existence of a market could create troubling incentives. Japan’s regulators are likely to examine whether surveillance systems, position limits, disclosure rules and dispute-resolution mechanisms are strong enough to reduce those risks.