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Datadog Shares Surge 23% After Revenue Beat and Strong AI Demand

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Datadog shares soared 23% on Thursday, marking the company’s second-best trading day ever, after the cloud software firm posted third-quarter results that exceeded Wall Street expectations and projected robust growth for the final quarter of the year.

The New York-based company reported $885.7 million in Q3 revenue, up 28% year-over-year and well above analyst estimates of $852.8 million, according to LSEG data. For the current quarter, Datadog forecasts between $912 million and $916 million in revenue, surpassing Wall Street’s $887 million projection.

Adjusted earnings reached 55 cents per share, topping FactSet estimates of 45 cents. The company also recorded net income of $33.9 million, or 10 cents per share, compared to $51.7 million, or 14 cents, a year earlier.

CEO Olivier Pomel credited the company’s momentum to continued innovation in artificial intelligence (AI) and cloud security tools. “The Datadog R&D team is innovating rapidly to help our customers solve problems in the AI space,” he said in a statement.

Datadog has rolled out a series of AI-focused products this year, including Bits AI Agents for SRE, which can automatically investigate system alerts and generate response drafts, and expanded features for LLM Observability, designed to monitor large language models. The firm also unveiled its MCP Server, which connects AI agents to enterprise data sources, and TOTO, its proprietary foundation model.

The company said the number of customers generating over $100,000 in annual recurring revenue rose 16% in the quarter, signaling sustained enterprise adoption.

Nvidia Becomes Member of India Deep Tech Alliance Amid $850M Funding Commitment

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Nvidia has officially joined the India Deep Tech Alliance, a growing coalition of investors dedicated to supporting deep-technology startups in the country. The announcement came on Wednesday as the alliance added several new members and secured over $850 million in additional capital commitments, aiming to close the funding gap for high-tech ventures in South Asia.

The new cohort of investors includes Qualcomm Ventures, Activate AI, InfoEdge Ventures, Chirate Ventures, and Kalaari Capital. Their involvement signals increased confidence in India’s deep-tech ecosystem, particularly in sectors such as artificial intelligence, robotics, semiconductors, and space technology.

The India Deep Tech Alliance was originally launched in September with a $1 billion initial commitment to back companies at the cutting edge of technology. Nvidia’s participation aligns with its broader strategy of investing in AI and other frontier technologies while expanding its footprint in emerging markets.

By joining the alliance, Nvidia and other new members aim to accelerate the growth of Indian startups, providing not only funding but also strategic guidance and technical expertise. This collaborative effort is expected to strengthen the country’s position as a global hub for deep-tech innovation.

Tinder Eyes Camera Access to Enhance Match Recommendations

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Tinder is experimenting with a new AI feature called Chemistry, aimed at easing the fatigue users often feel while swiping for matches. This feature is designed to analyze a user’s camera roll—once explicit permission is granted—to suggest highly relevant profiles each day. By leveraging personal images, Chemistry intends to better understand users’ preferences and surface compatible matches, potentially streamlining the search for meaningful connections. However, the approach may spark privacy concerns, particularly around the handling of sensitive or personal images.

The feature was revealed during Match Group’s third-quarter earnings call, where executives described Chemistry as a “major pillar” of Tinder’s product strategy for 2026. Chemistry combines interactive prompts and AI-driven analysis to learn about a user’s personality, style, and interests. By integrating multiple signals, the system aims to reduce the randomness of traditional swiping and increase the relevance of match suggestions.

According to the company, users will have to explicitly grant access to their camera roll before the AI can process any images. Once authorized, deep learning algorithms analyze the content to infer preferences and generate a curated set of matches. The service promises to present only a small number of high-quality profiles each day, rather than overwhelming users with endless swiping options.

While Chemistry could improve match accuracy and engagement, it also raises questions about data privacy and consent. Users will need to trust that sensitive photos are handled securely and not misused for other purposes. As AI-driven matchmaking becomes more sophisticated, balancing personalization with privacy will be critical for user adoption and trust in the platform.