When the agent workflow begins to disrupt traditional business models, the software stocks of "AI + cloud data platform" are rising.

Senior analysts from Wall Street financial giant Citi believe that by the end of 2026, the intelligent cloud data platform software companies focusing on "AI-driven metered data billing and cloud core resource consumption" behind "Agentic workflows" (i.e., AI agent-driven workflows) will be the most important performance growth and valuation uplift main players in the global stock market software sector. Citi analysts predict that AI agent workflows will become the dominant force in enterprise software and cloud computing spending, with the most direct beneficiaries undoubtedly being top cloud computing manufacturers (hyperscalers). Meanwhile, software companies that focus on "AI + cloud data platforms" with actual data/usage billing will be the biggest winners in this wave of software and cloud spending such as Oracle, Snowflake, MongoDB, and Elastic, among other industry leaders. Therefore, Citi's overall allocation preference for the software sector is weighted towards "AI + cloud data/cloud monitoring" usage-based intelligent cloud data platforms, while being cautious towards closed and increasingly expensive all-in-one AI application platforms. Agentic Workflows refer to systems in which AI agents independently complete multi-step tasks such as data extraction, analysis, and final decision-making. According to Citi, global enterprises are transitioning from "AI experimentation" to the "early stages of scaled deployment of AI agents." This trend is predicted to drive a 20-35% increase in enterprise software and cloud computing spending in 2026 following a 50% growth in 2025. Citi analysts note that clients are showing increasingly strong usage of AI data engineering tools, as evidenced by the significantly accelerated adoption of Snowflake Intelligence/Cortex (AI function modules). Feedback from the front lines indicates that a single AI use case can bring about $200,000 in incremental consumption, and some large customers may have more than 10 use cases, corresponding to significant usage and spending growth. For example, a major U.S. airline is testing Elastic for AI cloud monitoring, planning to replace Splunk. They also see strong customer demand for Elastic's "AI + cloud security" capabilities, hoping to achieve endpoint consolidation through their security product integration. Previously, cloud computing and search engine leaders such as Google, AI application leader Applovin focusing on "AI + digital advertising," AI data analysis leader Palantir, and the parent company of Facebook and Instagram, Meta as well as leading American cloud software giant Salesforce have announced incredibly strong performance data and future performance outlooks. This indicates that not only the demand for AI infrastructure, represented by NVIDIA's AI GPUs, is incredibly strong, but also the demand for AI software applications, especially those able to comprehensively enhance B2B operational efficiency, is high and rapidly permeating various industries. The current trajectory of AI application software development focuses on "generative AI applications" (such as DeepSeek, ChatGPT, Sora, and Claude released by Anthropic, among other globally popular AI applications) and the shift from question-and-answer chatbot-style AI to "AI agents capable of autonomously performing various tedious and complex tasks." There is a pressing need among enterprises to improve efficiency and reduce operational costs, thus driving significant progress in the widespread adoption of generative AI applications and AI agents. AI agents are likely to be the major trend in AI applications by 2030, signaling the evolution of artificial intelligence from an information assistant tool to a highly intelligent productivity tool. In the narrative of the booming AI application sector in the global stock market, Google's release of Gemini3 has caused a sensation worldwide, along with the strong performance and outlook announcements of cloud computing service companies such as Cloudflare Inc. focusing on "connectivity cloud" positioning, and software leaders like Salesforce and MongoDB. These signals indicate the active expansion and penetration of the latest AI applications among enterprises and consumers, validating the feasibility of AI application stories and preheating the potential accelerated growth trend post-2026. Google's Gemini3 has ignited a new wave of AI applications! GPT-5.2 is about to launch a game-changing move According to the analysts at Citi, companies like Snowflake and MongoDB, which focus on "AI + cloud data intelligence platforms," are transitioning from a "grand narrative phase" to a "phase of performance and cash flow realization growth." The embryonic form of a "super bull market" for these stocks is accelerating; combined with Google's blockbuster launch of Gemini3 AI application ecosystem in late November, this cutting-edge AI application software has caused a global sensation, driving a massive surge in Google's AI processing demand, while MongoDB's previously announced strong performance demonstrates a trajectory of robust growth akin to that of NVIDIA. Gemini3, launched by Google, has undoubtedly sparked a new wave of AI applications globally. The Gemini3 series of products brought a massive volume of AI token processing immediately upon release, leading Google to significantly reduce the free access volume of Gemini 3 Pro and Nano Banana Pro, and impose temporary restrictions on Pro subscription users. Coupled with recent trade export data from South Korea showing a sustained strong demand for HBM storage systems and enterprise-level SSDs, this further confirms that the heat of the AI trend is still in the early stage of constraining AI processing power infrastructure. It is worth noting that the developers of ChatGPT at OpenAI are not backing down. As the AI application wave intensifies, under the fierce competition between Google and OpenAI, the wave of AI applications is expected to rapidly spread to various industries, which bodes well for the long-term growth prospects of software companies like MongoDB focusing on "AI + cloud data platforms." Currently, eagle-eyed netizens have discovered hints of GPT-5.2. A leaked screenshot from the developer community shows GPT-5.2 and GPT-5.2-thinking options prominently placed in the Cursor's model drop-down menu. This suggests that developers at OpenAI may have realized that AI programming is not only a killer application for large AI models but also a domain that best showcases model reasoning capabilities. It is evident that a fiery "AI application super war" between Google and OpenAI is about to begin. Many clues suggest that GPT-5.2 has significantly surpassed Gemini 3. A comparison chart circulating on social media shows that GPT-5.2 almost completely outperforms Gemini 3 and Claude 4.5 in core metrics of large models. Sam Altman, the head of OpenAI, has also claimed internally that the new AI model will lead in reasoning capabilities compared to Google's competing product. The high-quality growth earnings data of MongoDB, combined with the recent strong revenue growth trend of Google Cloud Platform shown in Google's financial report, the explosive growth in Token volume of the Gemini series products, and Google's massive AI capex (AI-related capital expenditures), collectively illustrate that the demand for cloud data platforms bridging the gap between AI application development and deployment and the cloud-based AI inference power demand at companies such as Google are operating in a very robust economic environment. More importantly, with the introduction of the Gemini series AI products, including Nano Banana Pro, which have made a sensational debut and rapidly gained popularity among global enterprises and consumers, the entire "Google AI ecosystem" including MongoDB remains on a trajectory of robust performance growth. MongoDB, leveraging its integration with Atlas and Google Cloud/Vertex AI, is a core beneficiary of the unprecedented AI super wave, as the AI application stack represented by Atlas+Vertex AI accelerates towards practicality and scalability. This points to a surge in demand for practical AI applications, AI search, AI recommendations, and AI agents in both B2B and C-to-C sectors, while agent-based AI workflows are accelerating penetration between enterprises and consumers. MongoDB's recent financial report in early December has far exceeded Wall Street's general expectations, and the company has provided an incredibly strong guidance for the next fiscal quarter and the full year. MongoDB not only develops database software itself but also offers a suite of cloud computing services and commercial support around the database. The exceptional performance data stemming from the deep integration of MongoDB Atlas on the Google Cloud Platform highlights MongoDB's profound relationship with Google a crucial factor in its strong performance report. MongoDB is riding on the super tailwind generated by the surge in AI inference power demand on cloud platforms like Google. MongoDB Atlas, as a managed database, operates natively on the Google Cloud Platform and integrates deeply with core data/AI developer ecosystem services such as Vertex AI and BigQuery. MongoDB's own website mentions a case study where the vast majority of AI startups use Google Cloud (Google Cloud) and MongoDB Atlas as their main database and cloud-based AI infrastructure to run a comprehensive AI ecosystem platform based on large language models (LLM). Citi analysts noted that the feedback from AWS re:Invent 2025 suggests that the real winners of AI are transitioning from "model companies that tell stories" to "cloud data and observability platforms that handle Agentic workflows." Citi's investment stance on the software sector is clear: overweight usage-based cloud data/security/observability platforms and top cloud manufacturers as the software-based infrastructure for the AI application era; while exercising caution towards closed, expensive all-in-one AI platforms like Palantir, emphasizing a focus on performance and contract renewals rather than just sentiment and storytelling. The latest revenue and profit data from MongoDB, coupled with Google's massive AI Capex and the strong revenue growth of Google's cloud computing business, suggest that both the cloud data platform needs for deploying AI applications and the cloud-side AI inference processing power needs at companies like Google are operating in very robust economic environments. Gemini now has over 650 million monthly active users, and the total monthly Token volume has increased more than 20 times in a year, indicating that Google's entire AI ecosystem is thriving along with the explosive revenue data from the Google Cloud Platform and the strong demand for cloud-end AI inference power. The narrative of AI investment thesis is evolving from the "power story" to the "agentic workflows and data usage monetization driving strong revenue creation." After experiencing the first stage of AI investment dominated by power and infrastructure in 20232024, the market is clearly shifting towards the main theme of "AI applications and cloud data intelligence platforms." Companies like Salesforce, MongoDB, AppLovin, Meta, Google, and others are reporting revenue and profit growth that exceeds expectations in advertisement efficiency improvement, marketing automation, cloud data intelligence platforms, and AI native applications in developer and enterprise workflows. Some companies have already shown increased customer value from the widespread adoption of AI agents, increased usage times, and significant increases in cloud resource consumption. As quoted in Citi's Re:Invent feedback, cloud manufacturers and independent software vendors are restructuring their business models around "consumption-based billing + cloud data intelligence/AI workload actual data usage." This essentially means that AI is no longer just a capital story but is transforming into measurable revenue elasticity and a longer growth curve. Companies that can more reasonably integrate AI-driven cloud databases, cloud monitoring/observability platforms, marketing and advertising intelligent deployment platforms, and large-scale internet/cloud platforms that have their own AI large models and complete ecosystems for AI application development and deployment into an AI-driven model will likely become the core assets in the new wave of AI applications. These companies can share the dividends of AI inference demand in the era of large-scale AI and ensure long-term customer resource locking through platform ecosystems to sustainably improve profit margins and cash flow quality. The growth logic of MongoDB has shifted from a "general-purpose document-based database" to a cloud database technology route clearly linked to Google Cloud Platform + Gemini series AI application software, from the development to the deployment stage of a complete AI ecosystem. The deep and long-standing collaboration with Google is a crucial part of its strong performance report. MongoDB stands at the forefront of the tremendous tailwind created by the surge in demand for AI inference power on cloud platforms like Google. MongoDB Atlas, as a managed database, operates natively on the Google Cloud Platform, and is deeply integrated with core data/AI developer ecosystem services such as Vertex AI and BigQuery. MongoDB's official website mentions that the majority of AI startups use Google Cloud (Google Cloud) + MongoDB Atlas as the main database and cloud-based AI infrastructure for running a complete AI ecosystem platform based on large language models (LLM) successfully deployed to the cloud. Citi analysts state that the feedback from AWS re:Invent 2025 indicates that the true winners brought by AI are transitioning from "model companies that tell stories" to "cloud data and observability platforms handling Agentic workflows." Citi has a clear investment stance for the software sector: to overweight cloud data/security/observability platforms based on usage and top cloud manufacturers as the software infrastructure for the AI application era; while exercising caution towards closed, expensive all-in-one AI platforms like Palantir, emphasizing a focus on performance and contract renewals rather than just sentiment and storytelling. The latest strong revenue and profit data from MongoDB, as well as the increased AI Capex and strong revenue growth of Google Cloud Platform, echo each other. This demonstrates that the Google and other public cloud computing manufacturers are still operating in a highly active economic environment due to the strong demand for cloud inference AI power brought on pre-eminently by the accelerated penetration of AI applications at the cloud processing end. Gemini6.5Token20AIAIAI