Still More Of The Latest Thoughts From American Technology Companies On AI (2026 Q2)

A collection of quotes on artificial intelligence, or AI, from the management teams of US-listed technology companies in the 2026 Q2 earnings season.

Last month, I published Even More Of The Latest Thoughts From American Technology Companies On AI (2026 Q2). In it, I shared commentary in earnings conference calls for the first quarter of 2026, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. 

A few more technology companies I’m watching hosted earnings conference calls for 2026’s second quarter after I prepared the article. The leaders of these companies also had insights on AI that I think would be useful to share. This is an ongoing series. For the older commentary:

With that, here are the latest commentary, in no particular order:

Adobe (NASDAQ: ADBE)

In the Business Professionals and Consumers group, Adobe’s management is attempting to transform Acrobat from a leading PDF tool into an AI-powered document productivity platform; more than 400 billion PDFs are opened with Acrobat annually; management believes integrating AI into Adobe’s products will help customers realise the value of AI faster; Acrobat’s capabilities are integrated into major platforms including ChatGPT, Chrome, Claude, and Whatsapp; management recently announced major advances in Acrobat, powered by the Adobe Productivity Agent, that helps users understand information better and create work easily

Our differentiation is grounded in a deep understanding of the PDF format and three decades of document expertise to transform Acrobat from the world’s leading PDF tool into a comprehensive document productivity platform powered by AI. Today, people open more than 400 billion PDFs with Acrobat every year to read, edit and create content. By integrating AI deeply into our products, we are helping customers realize the value of AI faster, more accurately, securely and cost efficiently. We’re meeting users wherever they work and expanding our reach across the digital ecosystem by extending Acrobat’s capabilities to major platforms including ChatGPT, Chrome, Claude, Microsoft Edge and WhatsApp…

… Yesterday, we announced significant advances in Acrobat, powered by the Adobe Productivity Agent, that help people and teams understand the information in their documents faster, create polished work more easily and access trusted knowledge at scale. Acrobat now turns complex documents into visually rich interactive reports and summary slides, as well as audio formats like personal podcasts, enabling users to quickly absorb key insights. New features like Knowledge Base and Analyzer empower teams to ask questions across massive document collections and surface structured insights from thousands of files. This unlocks the value of existing organizational knowledge, driving smarter, faster decision-making at scale, all with enterprise-grade governance, security, trust and compliance. 

In the Creators & Creative Professionals group, Adobe’s differentiation lies in its deep domain expertise across media types; management is delivering personalised value to users with agentic AI with Firefly and Creative Cloud; Adobe offers intelligent routing to the best models for users’ needs; management has expanded the Adobe Creative Agent to additional Creative Cloud flagship apps; Adobe now has a new AI Assisted Editor in beta mode for users of its generative tools; management recently added native generation of music, speech and sound effects to Adobe Firefly; management recently released new creative skills and tools for social creators and solopreneurs; management recently enhanced Firefly AI Assistant’s personalisation and context-awareness; management recently added Adobe Firefly Graph Enterprise Edition and Firefly Creative Production Enterprise Edition to Firefly Enterprise; management recently added a new Simulate feature in Adobe Brand Intelligence to predict campaign performance before the content goes live; Disney Imagineering will be integrating Firefly Foundry for its theme park design toolkit; management is seeing a lot of usage in video generation in Firefly

Our differentiation is rooted in our deep domain expertise across media types, including imaging, design, video, photography, illustration, animation and 3D and complemented by our depth of intelligence across creative workflows. With Firefly and Creative Cloud, Adobe is reinventing creative software with agentic AI to deliver immediate, personalized value— meeting users where they are, with tools and agents that adapt to their needs. Adobe remains committed to delivering intuitive, context-aware experiences that honor creative craft, accelerate creative velocity and ensure that the next generation of Creators can thrive in an ever-changing world. Adobe’s strategy is to offer customers choice as well as intelligent routing to use the best models for their needs, fully integrated in Adobe applications, eliminating the friction of switching between workflows and platforms.

The Adobe Creative Agent empowers customers by handling the orchestration and execution of complex, repetitive creative workflows. We expanded the Adobe Creative Agent to additional Creative Cloud flagship apps, including Photoshop and Premiere, extending conversational, agentic workflows deeper into our professional applications.  

We also introduced several innovations in Photoshop, giving professionals more choice and control at every stage of the creative process. This includes a new AI Assisted Editor, now in beta, offering customers the option of using a dedicated interface and natural-language prompt bar alongside our generative tools powered by leading AI models. 

Adobe Firefly is the all-in-one creative AI studio for the next generation of Creators. In Q3, we added native generation of music, speech and sound effects, bringing commercially safe AI audio to creative workflows. We released new creative skills and tools like Create Storyboard and Create Brand Kit, purpose built for social creators and solopreneurs, along with additional enhancements that help make Firefly AI Assistant more personalized and context-aware over time…

…Firefly Enterprise, spanning Firefly Services, Adobe Firefly Foundry and Adobe Brand Intelligence, helps the world’s largest brands industrialize content production with brand safe custom models. In July, we expanded Firefly Enterprise with Adobe Firefly Graph Enterprise Edition and Firefly Creative Production Enterprise Edition, giving businesses reusable, nodebased creative workflows and mass-scale production capabilities, alongside a new Simulate feature in Adobe Brand Intelligence that predicts campaign performance before content goes live. We announced that Disney Imagineering is integrating Firefly Foundry into its theme park design toolkit, bringing Disney stories, characters and experiences to life in Parks faster than ever…

…If you look at the amount of innovation that is going into Firefly, it is magical. Video in particular, you are seeing a lot of usage of video, so that certainly helps across that.  

Adobe recently acquired Topaz Labs; Topaz Labs adds state-of-the-art AI enhancement models to the Adobe Firefly, Firefly Services, and Creative Cloud apps; Topaz Labs has over a million users; Topaz Labs’ AI technology has won an Emmy Award; Adobe’s management will integrate Topaz Labs’ technology across Adobe’s creative AI portfolio; management expects the deal to close in 2026 Q3 (FY2026 Q4); some of Adobe’s customers are also Topaz customers and they have been praising Topaz

We announced our agreement to acquire Topaz Labs, which would add state-of-the-art AI enhancement models in Adobe Firefly, Firefly Services and Creative Cloud apps, providing Creators, designers, video professionals, photographers and enterprises the tools to achieve exceptional quality across every format and workflow. With over a million users, Topaz Labs and its Emmy Award-winning AI technology will be integrated across Adobe’s creative AI portfolio, enabling customers to enhance footage, restore and remaster archival content, and blend AI-generated and traditionally captured content into seamless final productions. We expect the transaction to close in Q4, subject to regulatory approvals and other customary closing conditions…

…A number of our customers have been using our technology along with Topaz. We had a lot of great feedback on them from our customers.

Under the Creators & Creative Professionals group AI credit consumption is accelerating sequentially in 2026 Q2 (FY2026 Q3) across Creative Cloud and Firefly; Firefly’s ending ARR in 2026 Q2 (FY2026 Q3) was up 40% sequentially (was nearly $300 million in 2026 Q1); under the Marketing Professionals group, ending ARR was up 20% year-on-year in 2026 Q2 (FY2026 Q3) for each of AEM & agentic web apps, Adobe GenStudio and AEP & apps  

Additional Creators and Creative Professionals highlights include:

  • Creative freemium MAU now surpassing 100 million and growing over 70% year over year;
  • Intensity of AI usage continues with credit consumption accelerating quarter on quarter, across Creative Cloud and the Firefly App;
  • Firefly ending ARR across Firefly App and Firefly credit packs grew 40% quarter over quarter;
  • Enterprise wins this quarter include Academy Sports, Disney, Jet2, Perficient, Premier League, Publicis, Tennis Australia and T-Mobile…

…Additional Marketing Professionals highlights included:

  • Ending ARR growth of over 20% year over year for each of AEM & agentic web apps, Adobe GenStudio and AEP & apps;…
  • …Q3 industry analyst recognition, being named the top leader in two Forrester Waves: including Customer Data Platforms for B2C and Experience Optimization Solutions, as well as two IDC Marketscapes for Worldwide AI-Enabled Customer Data Platforms for B2B Users and B2C Users; 
  • Global enterprise customer wins in Q3 included Academy Sports, Alpine Racing, BNP Paribas, Humana, IKEA, Jet2, Marriott, MSC Cruises, Publicis, Royal Bank of Canada, Vanguard and Wells Fargo. 

Adobe’s management’s recently delivered the first unified brand visibility solution, Adobe Brand Visibility, by combining Semrush’s AI visibility intelligence with Adobe’s agentic content optimization capabilities; Adobe Brand Visibility can tap on Adobe’s database of nearly 300 million real-world AI search prompts; Adobe Brand Visibility is seeing tremendous interest from brands to understand how they are showing up across leading AI platforms; management recently introduced GenStudio for Commerce Media Networks for retailers to provide self-serve, on-brand creative for advertising partners; management recently expanded Adobe Brand Intelligence so users can predict audience reactions to content before it goes live; CX Enterprise Coworker was recently made generally available; CX Enterprise Coworker coordinates AI agents and workflows across analytics, content creation and journey orchestration; CX Enterprise Coworker already has 1,700 customers and early adopters

In Q3, we delivered the first unified brand visibility solution bringing together Semrush’s AI visibility intelligence with Adobe’s agentic content optimization capabilities, grounded in a deep understanding of business and brand context. Adobe Brand Visibility is a comprehensive solution for Generative Engine Optimization that combines the industry-leading capabilities of Adobe LLM Optimizer with Semrush’s AI Optimization. We’re seeing tremendous customer interest in tapping our database of nearly 300 million real-world AI search prompts to understand how their brands are showing up across leading platforms such as ChatGPT, Google AI Mode, Microsoft Copilot and Perplexity AI. 

At the Cannes Lions International Festival of Creativity in June, we introduced a major expansion of Adobe GenStudio to help brands meet customers wherever they are in the moment of purchase, in a stream or in a scroll. GenStudio for Commerce Media Networks gives retailers a way to attract advertising partners with self-serve, on-brand creative built directly from product listings and category context. We also expanded Adobe Brand Intelligence capabilities so marketing teams can preview how content will resonate with audiences before it ever goes live. This is what an agentic content supply chain demands, at the speed our customers need. 

We are seeing tremendous customer interest in CX Enterprise Coworker, the specialized AI agent in CX Enterprise that autonomously executes complex marketing and customer engagement workflows. CX Enterprise Coworker, which became generally available in June, meets marketers where they are, embedding agentic intelligence directly into the engagement lifecycle, offering choice and control while enabling teams to act faster, scale personalization, and continuously optimize outcomes. CX Enterprise Coworker synthesizes insights from Adobe and third-party applications, while coordinating AI agents and workflows across analytics, content creation and journey orchestration. Early customer interest is strong with over 1,700 customers and early adopters of CX Enterprise Coworker. 

Adobe’s management is seeing customers look for 4 things in agentic software, namely, (1) a user interface of their choice, (2) model flexibility, (3) connection to their enterprise data so that there’s no hallucination, and (4) the ability to realise the value of the software

What I hear is when they look for agentic software, what they mean by that is they are looking for, one, the product architecture, the product architecture evolving so that people can use the user interface of their choice, whether it is conversational or a traditional interface, that could be ChatGPT or a cloud or a Copilot, along with the functionality they get from Adobe. We are making that possible, as you have seen our announcements across creativity, across productivity, as well as the CX announcements like the Adobe Marketing Agent. We are making that possible through other interfaces as well as our own, like we are doing with our apps as well.

Second, they are looking for model flexibility. They’re looking to make sure that they can have access to the best models available and that we, as the provider of the agentic software, are matching the best model available to the best task and making it easy for users. That’s something that we are doing extremely well, with the Adobe Creative Agent we talked about and the Adobe Productivity Agent as well. 

Then they’re looking to make sure that the apps are using their enterprise data and context so that there’s no hallucination, that they get usable results, and that they get predictability out of the use of AI. That’s something that we are doing really well with, for example, the CX Enterprise Coworker we talked about, because that’s built on top of the Adobe Experience Platform, where we serve over 1 trillion experiences every year.

Then they’re looking for value. They’re looking to make sure that the provider of agentic software is helping them realize the full value of this software, bringing it all together across all of the complexity of AI and software. That’s what we’re doing with the growth we’re seeing across our forward -deployed engineering and our value -realization teams.

Adobe’s management sees a wide variety of pricing models for its AI products

[Question] One question we get a lot is you kind of meet customers where they are or how they want to consume in terms of how you monetize AI across either Creative Cloud, Acrobat, or your enterprise products, and that is through bundled credits, credit packs, premium tiers, per-seat pricing. There is also usage-based contracts. Over time, which pricing model do you expect to become the primary driver of AI revenue?

[Answer] It really depends on what exactly they are doing, what are they trying to get done, what is their skill level, what they are doing it in the context of. So that can vary based on whether they are individuals or enterprise customers and so on. So it really then starts to focus on what they are actually doing with it. So we are happy to work with, say, for example, something like Microsoft Copilot in the enterprise to ChatGPT for consumers and so on, and making sure that they are getting value and driving up that intensity of usage and the engagement. Beyond that, depending on the product area, once we have the value, we can map it to the monetization that Shantanu talked about, which helps us with better segmentation, understanding what the segments are, and how they are getting value. So we have a wide variety of ways, as you pointed out, to monetize that, and we are going to be applying that both based on the context of the customer and the context of what they are doing.

MongoDB (NASDAQ: MDB)

Enterprise Advanced (EA) revenue growth was 36% year-on-year in 2026 Q2 (FY2027 Q2); the strength in EA was widespread across MongoDB’s installed base; when management brought search and vector search to EA, demand for EA came in immediately from customers who want to build AI in their own self-managed environments; a US bank was an existing customer of EA and recently started using EA for GenAI and semantic search; AI self-managed use cases represent net new demand for MongoDB, while hybrid development opens the door to Atlas for existing EA customers; the introduction of AI features in EA was driven by customer requests

EA and other had a standout quarter, growing 36% year-over-year due to widespread strength driven by our run anywhere capabilities…

…Turning to Enterprise Advanced, this quarter’s strength was widespread across our install base, particularly within financial services, tech, and the public sector. Two patterns in how customers are using EA stand out, and both point to why this business is strategic for us.

The first is AI in governed, self-managed environments. This quarter, we brought search and vector search to EA, closing a gap between our cloud and self-managed experiences. Demand came in immediately and across industries from customers looking to take a consolidated approach to building AI in their own governed, self-managed environments. A major U.S. bank shows what that looks like in practice. EA already serves as the standardized data platform for more than 100 production applications across payments, fraud detections, document processing, customer and account services. This quarter, that bank extended that same environment to GenAI and semantic search for employee advisors, chatbots, product search, and document intelligence. By bringing operational data, search, and vector retrieval together, self-managed with EA, they keep sensitive customer and conversational data inside their own governed environment without sending up separate systems. That gives them a practical foundation to expand AI across the bank on the same platform already running their most critical operations…

…Bringing AI self-managed opens net new demand for us, and hybrid deployment often means that the strong EA estate opens the door to net new Atlas conversations within the same customers…

…The reason we invested in EA roadmap that we outlined, and it is nice to see it is working out, is that customers said to us, many customers, even in my early days, that you must invest in EA. If EA gets to being AI-ready with search, vector search and so on, they are asking, “Hey, can we also make Voyage AI available in a self-managed type of an environment?” That was very customer-driven, and we are meeting customers where they are.

Voyage’s customer count was up nearly 100% sequentially in 2026 Q2 (FY2027 Q2); Atlas Vector Search adoption  is outpacing growth in the rest of MongoDB; Atlas Vector Search’s performance is a sign of strong early momentum in AI workloads; financial services, healthcare, technology, and AI native companies are increasingly choosing MongoDB to run AI workloads; Voyage’s embedding and reranking models are consistently at the top of leaderboards; management recently brought automated Voyage embeddings to Atlas; management recently launched Voyage Code 4, a coding model; management recently shipped a new reranking API for Voyage; some of Atlas’s largest existing customers are starting to use Voyage for AI use cases, while a large number of new Voyage customers are AI natives that are new to MongoDB; Voyage grew its customers by around 100% sequentially for the 2nd consecutive quarter in 2026 Q2 (FY2027 Q2); Claude Code and Codex are driving developer referrals to Voyage; coding agents love Voyage; there is low awareness that Voyage is part of MongoDB, but once customers realise this, they start to also look at Atlas

Voyage customer count nearly doubled quarter-over-quarter, and Atlas Vector Search adoption continues to outpace the growth of the rest of the company, showing our strong early momentum for AI workloads…

…Enterprises across financial services, healthcare, tech, are running their most demanding mission-critical workloads on MongoDB, and we are winning more workloads each quarter. Increasingly, these same enterprises, as well as AI natives, are choosing our platform for AI workloads, evidenced by the adoption of Atlas Vector Search and Voyage AI embeddings…

…We are also seeing strong traction with Voyage, our embedding and reranking models, which consistently rank at the top of independent leaderboards. In August, we brought automated Voyage embeddings to Atlas for one-click vector search setup, launched Voyage Code 4, a model purpose-built for code, and shipped an upgraded reranking API, all keeping Atlas retrieval accuracy for AI ahead of the market. 

Voyage traction is showing up on both ends of the market. Some of our largest existing Atlas customers are beginning to adopt Voyage for AI use cases, while a large majority of new Voyage customers are AI natives and have no prior relationship to MongoDB…

…Within Atlas, Voyage customers roughly doubled quarter- over- quarter for the second consecutive quarter, continuing the encouraging signs of the demand for our AI embedding capabilities…

…When I look at the names of the kind of customers we are getting, whether they are in San Francisco Bay Area, whether they are large enterprise, whether they are in London or Tel Aviv, or Seattle, they tend to be driven by AI workloads. When the team did analysis on where is the referral for our Voyage is coming, as you would have imagined, most of this referral is coming via coding agents. Number one, Claude, and number two, Codex is driving most of the referral traffic for Voyage…

…Coding agents love Voyage. They are recommending us, and we are getting this new customer cohort…

…The awareness is low, that Voyage is actually coming from MongoDB. This customer told me, “Oh, we love Voyage. We are using Voyage.” I said, “You know that is a MongoDB product.” They are like, “Oh, we did not know that.” Okay, then we should now look at Atlas because you have Atlas auto-embeddings.

MongoDB’s management thinks it’s natural for customers whose data are already on MongoDB to build agentic workflows on top of the data; MongoDB’s platform have important features for AI, such as search, vector search, and embeddings, that are built in; management is seeing different industries utilise MongoDB for a range of AI use cases; management is seeing a growing number of AI workloads reach production; MongoDB is starting to see some benefit from AI but it’s still small; the AI workloads management is seeing on MongoDB are mostly for customer-facing workloads; one of MongoDB’s bank customers told management that they think vectors should be integrated into the data layer, which is to MongoDB’s advantage; a media company which is a big vector search customer, had its agents realise that semantic queries need to be within an operational data layer; Eleven Labs, an AI native that continues to scale well with MongoDB, thinks it’s important that vector is being embedded in a database

For customers that already run large part of their data estate on MongoDB, building an agent on top of that data is a natural extension because the data an agent actually needs is live operational data, not a stale copy sitting in a warehouse. Search, vector search, and embeddings are built in, not bolted on, so rather than agents connecting to many separate systems, they connect to one platform. We are seeing this show up across industries in a range of use cases, whether it is retrieval of internal knowledge, customer-facing chatbots and agents, or fraud and identity workflows. It is still early, but we are seeing more of these workloads reach production, such as the Financial Times…

…We have started to see some benefit from AI, even though it is small, but we are excited about the momentum, and we do expect consumption to continue to be consistent with what we have seen during the first half of the year…

… What I’m seeing is initially, say you are a wealth manager at a bank and there are lots and lots of knowledge base articles that you want to vectorize, use our embeddings, and then use as a chatbot for folks that are doing wealth management and talking to clients real-time. That is one very specific example where a particular large bank is using MongoDB. There are also other examples where because there are lots and lots of documents, employee-facing use cases where knowledge base articles so that employees can leverage, do a search, because now search is fully integrated into the operational data and documents get loaded, and then embeddings make the vectorization better. That will be another large enterprise example where we are seeing use cases. But the clarity that I got was that it was almost always, “Hey, we want to use MongoDB where the scale matters on the agents that we are trying to create for our customer-facing activities,” whatever the customer-facing activities are. We are not seeing early traction with, “Hey, I created a co-pilot kind of a thing that appeals to a couple of hundred employees.”…

…These are millions of agents in production that are doing something that is customer-facing, and they would say, “We want to use MongoDB for scale, performance, and of course, run anywhere,” and that’s where you’re using it…

…This large bank told me that based on their testing, they believe that vectors should be integrated fully in the operational data layer, and MongoDB doing that was seen as a huge advantage…

…Even a large media company, which became one of our biggest vector search customer, that was driven by an agent trying to do the semantic query and figuring it out. Okay, if this is an operational data layer, then it just works…

…We are seeing that even in AI native cohort, that vector being part of the database is received really, really well. One of the examples I shared, last quarter, ElevenLabs, which continues to scale nicely with MongoDB, they see that as a huge advantage of vector being embedded.

The Financial Times is using MongoDB for AI-driven discovery; the Financial Times is using Vector Search and Voyage AIto build a hybrid full text and semantic search solution; the Financial Times has used the Voyage-4 and Voyage-4-lite models to significantly cut retrieval costs with minimal performance impact; the time-to-value for the Financial Times in using MongoDB is measured in weeks

It is still early, but we are seeing more of these workloads reach production, such as the Financial Times, which leverages us to power AI-driven discovery, reaching millions of readers with interactive experiences at scale. With Vector Search and Voyage AI, the Financial Times now unifies their operational data and vector embeddings on a single platform, building a hybrid full text and semantic search solution, eliminating the complexity of syncing separate systems and accelerating time to production. By indexing content with the high accuracy Voyage-4 model and serving over 100,000 daily queries on the cost-efficient Voyage-4-lite model, the Financial Times has significantly cut retrieval costs with minimal performance impact. What used to take weeks of manual index monitoring is now finished in a day…

…Like what we saw on my Financial Times use case, that I shared, the time to value for them to leverage Atlas Vector Search and embedding was in weeks, not in months and years, to get AI ready for searches and others that happen.

Frontier AI labs are customers and partners of MongoDB; multiple frontier labs are using Atlas for mission-critical workloads; there’s one frontier lab using MongoDB for inference and chat workloads after poor performance from PostgreSQL, and experiencing 10x faster reads; frontier labs are using MongoDB for research workloads for model development; MongoDB’s relationships with frontier labs are still early, but management is optimistic about the traction; MongoDB’s management recently launched a fully managed MCP (model context protocol) server for developers to connect to MongoDB when using agentic coding platforms; Anthropic’s management have been pointing developers to MongoDB Voyage for embeddings; the frontier lab using MongoDB for inference started with inference, and then expanded usage to other workloads; the frontier lab using MongoDB for inference is having a great experience with Atlas

Moving on to the momentum we are seeing with Frontier Labs, who are both customers and partners for us. Multiple leading labs leverage Atlas for workloads that are mission-critical to how they ship their products. One lab uses us for inference and chat workloads after moving away from PostgreSQL due to performance lags and outages affecting user experience. They migrated their chat memory system onto Atlas in just four weeks and now run at 10x faster reads than PostgreSQL. Beyond that, labs use us for research workloads to store experimental results, evaluation data, and training artifacts for model development. These relationships are still early, and engagement varies lab by lab, but we are energized by the traction we are seeing with them…

…Just recently, we launched a fully managed MCP server, making it easier for developers and agents to connect directly to MongoDB when they are using Claude Code, Codex, and Grok Build, as well as popular coding tools like Cursor and Devin from Cognition. Paul Smith, Chief Commercial Officer at Anthropic, described our technology partnership and recent integration with Claude by noting, “The best AI applications need a strong database, which is why we have long pointed to developers building on Claude to MongoDB Voyage for embeddings. More recently, demand from those developers drove MongoDB to build a new managed MCP server, which has seen fast adoption since launch and now lets developers explore, query, and manage their MongoDB data without ever leaving Claude.”…

…With one of the labs, they started towards the later half of last calendar year with one of the workloads that was running inference on MongoDB and used us as a memory layer. With that lab, our team, what they saw on the Atlas performance for that specific inference, then they said, “Wow, Atlas is performing really well across reads and writes compared to PostgreSQL.” That is what they were using originally. They started then moving just recently in Q2, few other workloads for inference on Atlas. So we had one inference workload that started last year in November, December timeframe, and then they moved another couple of workloads for inference for some other products that they have created in, I want to say this is August, so around June, July timeframe. We are seeing. They told me straight up, this is the technology team, that Atlas has taken all the pain away from an uptime perspective, performance perspective. We do not even think about it. We are now as we create new products, we want to run inference on it.

MongoDB’s management thinks the choice of the data layer made by AI natives will determine whether their product can rapidly scale; some AI natives choose MongoDB from day one, while others migrate to MongoDB after hitting scaling limits when using their prompt-driven development platforms; many of MongoDB’s new customer additions in 2026 Q2 (FY2027 Q2) are AI natives

The final piece of the AI opportunity is AI natives, companies whose data layer determines whether the product can support rapid scale. Some choose us from day one. Others start elsewhere, like prompt-driven development platforms, and migrate to us as they hit scaling limits and real usage arrives…

…We added a record 2,900 net new customers this quarter, and many of them are AI natives.

Fireflies, an AI native building an AI assistant for work, serves more than 20 million users; Fireflies chose MongoDB from day one for its flexible document model, which has provided the foundation for Fireflies’ hypergrowth

Fireflies, a unicorn AI native startup, is building what it calls the number one AI assistant for work, helping people unlock the knowledge buried in their conversations. Fireflies serves more than 20 million users across 1 million+ organizations and has processed over 7 billion meeting minutes. Fireflies chose Atlas from day one for its flexible document model over a rigid relational schema, and today runs more than 40 microservices with change streams powering real-time pipelines for analytics and growth intelligence. That lean, scalable foundation has helped fuel their hypergrowth seamlessly.

Eve, an AI native providing AI solutions for legal work, is using Atlas embedding and Voyage’s reranking API to improve retrieval quality directly in Eve’s RAG (retrieval-augmented generation) layer 

Eve is one of them. A unicorn AI native that automates legal case intake, medical chronologies, and demand letter drafting for plaintiff law firms. Eve uses Atlas embedding and reranking API powered by Voyage AI’s Rerank 2.5 to surface the most relevant evidence from large sets of case documents. This improves retrieval quality directly into Eve’s RAG layer while simplifying the infrastructure needed to build and evolve these AI experiences.

Oracle (NYSE: ORCL)

Oracle had very strong year-on-year revenue growth of 121% for its Cloud Infrastructure business in 2026 Q2 (FY2027 Q1), driven by a strong demand environment

Cloud infrastructure revenue for Q1 was $7.4 billion, up 121%, reflecting strong execution as we brought record levels of new megawatt capacity online, supported by a continued strong demand environment for compute and our database services.

Oracle’s gross margin declined in 2026 Q2 (FY2027 Q1) as expected as it builds out its AI infrastructure business; the buildout has caused Oracle’s free cash flow to be negative; management continues to expect Oracle’s capex to around $70 billion in FY2027; Oracle recently completed a $20 billion at-the-market equity issuance in 2026 Q2 (FY2027 Q1) to help fund its capex; management has previously said that FY2027 and FY2028 are peak capex years for Oracle;  Oracle’s AI infrastructure projects are delivering a free cash flow conversion ratio of 100% to post-tax EBITDA very shortly after they ramp up; management is seeing costs for data centers going up, and so Oracle has to charge more, so there won’t be an impact on gross margins; management thinks operating margin is actually more important for Oracle than gross margin; management expects Oracle’s gross margin to step down in FY2027 and then flatten over the next couple of years

Our gross margin did decline as expected, driven by impacts from ramping up our data centers and the acceleration of infrastructure revenue. However, this was offset in the quarter by lower operating costs and strong operating leverage tied to simplification and efficiency actions…

…Our CapEx for the quarter was $28 billion, leading to negative free cash flow of $5 billion. Our net cash CapEx, so net of prepayments, was $18 billion for the quarter. To note, our CapEx will not be linear throughout the year. We continue to anticipate $90 billion-$95 billion in CapEx for the full year, with not more than $70 billion in net cash CapEx. Lastly, we are quite pleased to announce that we completed our previously disclosed $20 billion at-the-market equity issuance in entirety during the Q1…

…[Question] You have told us that fiscal 2027 and 2028 are peak CapEx years. At the same time, others in the market are spending hundreds of billions of dollars on capacity with seemingly no end in sight. How should we think about Oracle possibly slowing down spending beyond the next two years if others aren’t?…

…What I would say, though, is that each of these projects that we are doing, by nature, is a strong free cash flow generating project. As soon as they ramp up, very shortly thereafter, they are delivering a free cash flow conversion ratio of something like 100% to post-tax EBITDA. In fact, the business by nature is somewhat, quote, “self-funding” at some point, in terms of throwing off a lot of free cash flow…

…Prices in a world where demand exceeds supply, typically prices don’t go down, they do go up. I think the net effect is that obviously things cost more, but then we have to charge more money for them so that we get compensated. We’re doing that across all of these different businesses. We don’t expect this to have an impact on our gross margins…

…Gross margin to me is always an important indicator, probably even more important internally for us to double-check, and I think investors obviously want to double-check. It is something that will change very quickly, for example, if we do not have the right pricing model. When we talk about driving value, though, and driving value for the business, for me, operating margin is probably the ultimate point that we want to follow. Gross margin, like you mentioned, at the moment, there is a number of things going on. We have both the ramp-up in data centers, plus we have an adjustment across the two business models that we have in the business. Software being a much higher gross margin business, but with higher R&D and sales costs below gross margin. Infrastructure being a lower gross margin business, and we have talked about that and we gave the numbers. Clay has given the expectations for quite a bit of that business. Not database, obviously, but the more AI infrastructure and cloud side. That business, by nature, has much lower R&D and sales associated with it, at least in a company like Oracle, where we can effectively gain from all of the R&D that is already been done and being done across the rest of the company. So really, how to watch how we are going to drive value out of the business over time, I think operating margin is really the key metric that we would look at…

…I mentioned in the Q4 that we would expect a step down in gross margins this year. You can see the EPS guidance that we give, though, so you can see what we might expect in terms of operating margin. Over the next couple of years, as we finish the ramp-up, you can reasonably expect that gross margin would flatten, I would say.

Oracle’s remaining performance obligation (RPO) in 2026 Q2 (FY2027 Q1) was up 46% year-on-year to $664 billion (was $638 billion in 2026 Q1); the vast majority of the sequential increases in RPO were from prepay or bring-your-own-hardware contracts, which do not require incremental capital from Oracle, although it still requires capex; the new S$26 billion in RPO will not impact Oracle’s capital expenditure or revenue until FY2028 and beyond; management expects half of Oracle’s RPO to convert to revenue in the next 36 months

our Remaining Performance Obligations, or RPO, increased $26 billion from Q4. There are two things happening here. First, we continued to grow our RPO during the quarter to support future revenues. The vast majority of those new contracts were via prepay or bring your own hardware or similar mechanic, so will not require incremental capital from Oracle. Also, that new RPO will not impact our CapEx or revenues until fiscal 2028 or beyond. Second, we started to see a strong conversion of our RPO into revenues this quarter, driving our cloud infrastructure results…

…I didn’t say, and I don’t think myself nor Hilary said that it doesn’t require additional CapEx. We said it doesn’t require additional cash from Oracle…

…While there clearly are capital expenditures, it does not require Oracle to go out and find additional cash to do it. Now, the question becomes, well, how do you do that? Well, we have a variety of different models. Sometimes it’s working with our suppliers, through different financing arrangements that allows us to pay for the capacity as the customers pay us. That’s one mechanism. Another mechanism is that a customer says, “Hi, I’d like to pay for the hardware, but use your operational ability and your cloud infrastructure technology assets and your data center to go out and actually turn that into an AI cluster.” It’s a different option. A third option is that the customer has been able to raise money. Maybe it is a startup, maybe it is an established company, and says, “Hi, I would like to pay you upfront as a prepayment, and in return, that doesn’t require you to front the cash to go out and spend your dollars on that CapEx.”…

…We now expect around half of our RPO to convert into sales over the next 36 months…

…We closed more than $30 billion of additional AI contracts in Q1 without requiring additional capital from Oracle.

Oracle’s management thinks that AI is an accelerator for packaged applications; management thinks AI agents can perform tasks using an organisation’s established workflows and business rules, and human employees just have to oversee the agents; management thinks Oracle can combine AI with business rules, regulatory compliance, security models, and data modesto enable customers to realise AI’s value; management is confident that the introduction of AI into Oracle’s product suite will deliver significantly faster ROI (return on investment) for customers; management will soon introduce an agentic AI feature that can dramatically reduce SaaS deployment times; management thinks Oracle AI Agent Studio has a very compelling value proposition because customers get to build AI agents on very complex business rules, a highly differentiated security model, and data models 

The introduction of AI is an accelerator, not a replacement, for packaged applications. As such, our decades of experience and expertise running business processes across every industry, in every geography, for organizations of any size, gives us the understanding of how to help them succeed.

Before AI came along, application suites had already proven their effectiveness. Companies had been able to increase their profit margins because end-to-end automation with standardized and efficient business processes proved to be much more effective than one-off custom solutions. But that did require organizations to follow workflows and processes as designed in the system, something that many struggle to achieve consistently across functions, teams, and regions. AI changes this dynamic. Rather than asking every employee to navigate and execute a process exactly as the system expects, AI agents can perform tasks using the organization’s established workflows and business rules. Employees then shift to overseeing agents, resolving exceptions, and applying human judgment where it matters most. By combining applied AI with decades of sophisticated business rules, regulatory compliance, security models, data models, and customer configurations, we enable customers to continuously realize AI’s value while keeping their data secure and their operational guardrails intact…

…We are incredibly confident in the potential for this new paradigm to deliver much more rapid ROI for our customers. At AI World in October, we will unveil a new agentic AI accelerator poised to redefine how customers deploy Oracle applications faster, simpler, and at a dramatically lower cost. Working alongside Oracle and customer teams, AI agents will automate and orchestrate implementation at an unprecedented scale, compressing SaaS deployments from years to months, and months to weeks…

…The next layer is our Oracle Fusion Agentic Applications AI studio, which allows customers and/or partners to build their own AI agents right inside the same platform. That is not a different platform. It is not a different control plane. It is the same control plane and the same platform that our applications are running on, which means that Oracle AI Agent Studio, which allows customers to build their own agents or partners, gets all of the same quarterly updates, gets all of the same security patching, and is available as a complete service to our customers. You are allowing customers to position AI as a UI on top of a very complex set of business rules, on top of a highly differentiated security model, and of course, data models that have evolved for years and years. You put the horizontal applications, the vertical applications, the Oracle AI Agent Studio together, and we think that is very compelling.

Oracle’s customers used embedded AI capabilities more than 150 million times in 2026 Q2 (FY2027 Q1), up 42% sequentially; Oracle’s AI agents executed 3.5 million times in 2026 Q2 (FY2027 Q1), up nearly 100% sequentially; Oracle’s customers have more than 2,300 AI agents in production, up 90% sequentially; AI production usage in Oracle Fusion consumed 900 billion tokens in 2026 Q2 (FY2027 Q1)

Customers used our embedded AI capabilities more than 150 million times during the quarter, with usage growing 42% sequentially. Our AI agents executed more than 3.5 million times in production during the quarter, nearly doubling quarter- over- quarter. Customers have over 2,300 AI agents in production, and that is up 90% quarter- over- quarter. Overall, AI production usage across Fusion alone consumed 900 billion tokens during the quarter. 

Oracle’s management recently announced the general availability of the AI-powered NetSuite Next; more than 10,000 NetSuite customers are already using the NetSuite AI Connector service that lets customers connect NetSuite data to AI assistants; Every Man Jack estimates that the NetSuite AI Connector service will save it $350,000 annually

We are announcing the general availability of our new AI-powered offering called NetSuite Next. This presents an agentic experience that is simpler, more powerful, and is infused with AI across the workflows that customers rely on every day. It is easier to adopt, it is more productive from day one, and it is more valuable as customers grow. Additionally, the NetSuite AI Connector Service, which lets customers securely connect their NetSuite data to leading AI assistants of their choice, including ChatGPT and Claude, is already one of the fastest adopted capabilities in the whole entire history of NetSuite, with more than 10,000 customers already using it. Personal care company Every Man Jack estimates that the service alone will save $350,000 annually and nearly 5,000 hours of work.

Oracle delivered 850 megawatts of AI infrastructure in 2026 Q2 (FY2027 Q1); delivery in FY2027 Q1 is 3x what was delivered in FY2026 Q4, and 73% of the capacity delivered in FY2026; Oracle’s global GPU utilisation is 97.9% (was 97.5% in 2026 Q1); Oracle’s GPUs that came up for renewal in 2026 Q2 (FY2027 Q1), most of which are at least 4 years old, was renewed or resold at prices 20% higher; management sees long useful lives for the AI infrastructure Oracle is building; Oracle’s Abilene, Texas AI data centre has delivered 75% of its total capacity (was 42% in 2026 Q1); customer-acceptance at Abilene has shortened to only 24 hours; OpenAI’s latest GPT-6 Astra model was trained in Abilene; Oracle’s Shackelford, Texas AI data center is progressing well; management finds NVIDIA’s Vera Rubin systems to be performing better than expected, and will deliver them to customers in 2026 Q3 (FY2027 Q2); Oracle’s AI infrastructure investments are pretty diversified; when Oracle builds a data center, the capacity is not all delivered at one go; Oracle’s new data centers in New Mexico and Wisconsin are making good progress; Oracle is deploying Bloom Energy’s fuel cells in New Mexico and management sees it as the most environmentally friendly on-site energy generation technology; management sees the current constraints in AI infrastructure as power and data centers

We delivered 850 megawatts of AI capacity containing more than 300,000 GPUs to customers since the end of Q4. Delivery in Q1 is almost three times what we delivered in all of Q4 and 73% of the total capacity we delivered last fiscal year…

…GPU utilization remains extremely high at 97.9% in Q1. GPU longevity and value continue to impress. Of all the GPUs that came up for renewal in Q1, that capacity was renewed or resold at a 20% premium to prior contracts. The majority of those GPUs are four years or older. We see a long, useful life with increasing value for the AI capacity we’re deploying.

Abilene continues to deliver at an extraordinary pace. We delivered 131,000 GPUs there in Q1, 1.9 times the volume delivered in Q4. Six of the eight campus buildings, representing 618 megawatts and 75% of total capacity, have now been delivered to the customer. Customer acceptance has compressed to only 24 hours, showing that the systems arrive ready for customer workloads. The recently released GPT-6 Astra was trained at our site in Abilene. Shackelford is our next gigawatt-scale campus and is progressing well.

NVIDIA Vera Rubin systems are performing better than expected across hardware quality, manufacturing yield, and performance. We will deliver our first Vera Rubin systems to customers in Q2…

…New Mexico and Wisconsin are very important large sites for us, but I think it is important to have some context. We talk about these sites as being around 1 gigawatt a piece. We just delivered 850 megawatts in Q1. What that means is that, as you can see, neither Shackelford, nor New Mexico, or Wisconsin, or Michigan were delivered in Q1. So we have a large, diverse, broad set of data center developments going on throughout the U.S. and around the world to deliver capacity to customers. Now, some of these sites, like New Mexico and Wisconsin, obviously garner a lot of attention. There is a lot of discussion about them. But I think it’s important people realize that all of our eggs are not in a single basket…

…When these large sites are built, they don’t all come online at once. Let’s say that you have a gigawatt site, and it’s supposed to start delivering, let’s say, in January of a year. It’s not like in January you get a gigawatt of capacity. It’s phased over many quarters…

…New Mexico is an interesting location. We’re making very good progress. In terms of construction, data center is definitely on track. We’re going through the process of acquiring our air permit. And the technology that we’ll be deploying there is Bloom fuel cells, which is by far the most environmentally friendly way that we can do on-site power generation. Has extremely low water consumption, has extremely low emissions compared to really any other way to do on-site generation. We’re very confident that as we continue through this process, we’ll work with the local regulators and community citizens in Doña Ana County, and with everybody else in New Mexico. But we’re just going through the process, and I don’t think that’s rare for large projects like this one.

In Wisconsin, we’re not doing on-site generation. We’re really working with our partners across the board to design and deliver that energy capability through the grid. But again, working through the process, these are complex projects. And again, in Wisconsin, data center delivery is actually very much on track and going well. Working with the Public Service Commission and ATC and We Energies, we’re constantly evolving different aspects of the energy design and delivery plan…

…The environment continually changes. It used to be that the constraints were GPUs and fabs. Then constraints moved to power generation. There’s data center constraints. But the world’s a big place…

…

Oracle has completed its Azure and AWS regional footprint expansions and this gives customers a consistent way to run Oracle AI Database next to their applications in the cloud they choose

We completed our planned Azure and AWS regional footprint expansion, reaching 70 multicloud database regions and 119 availability zones. This gives customers a consistent way to run Oracle AI Database next to their applications and data in the cloud they choose.

Oracle recently expanded its relationship with OpenAI and now offers OpenAI API access; Oracle is now bringing Gemini models to its enterprise applications; Oracle has released new Grok models; management is expanding Oracle’’s opens source model catalog

We expanded our OpenAI relationship to offer OpenAI API access, ChatGPT for work, and Codex through Oracle Marketplace, including GPT-6 Astra. We are bringing Gemini models to Oracle’s enterprise applications, and we released new Grok reasoning, multimodal, and text-to-speech models. We also continue to expand the open source model catalog, including new models from NVIDIA, Qwen, Google, DeepSeek, and others.

With APEXlang, developers can tap on the speed of generative AI for coding, but remove the downsides of difficulty in maintenance; Oracle’s AI Data Platform is now integrated with Codex and Claude Code; Oracle’s AI Data Platform has a few different business models, which includes consuming non-Oracle work

APEXlang is a new technology that represents an APEX application as structured, human-readable application definitions that can be stored in source control, validated, and governed. AI coding agents generate and modify those definitions while the APEX engine continues to provide the security, reliability, and operational controls required for enterprise applications. Developers gain the speed of generative development without the downsides of difficult-to-maintain opaque application code. We are taking the same approach with the Oracle AI Data Platform. AI Data Platform is now integrated with Codex and Claude Code, allowing developers to work with AI Data Platform data, knowledge, and capabilities from the coding environments they already prefer…

…[Question] You mentioned the AI data platform, you guys put it in your press release, so I just want to double-click on that. It seems like an important way in allowing customers to deploy agents against their proprietary data. Can you just help us understand the business model around that? Does it drive incremental consumption of Oracle Database OCI, or do you see that being sort of a standalone software revenue opportunity?

[Answer] You sort of clicked on the answer, really, to the business model is all the above. Certainly, we can run this in a model where we are consuming 100% of non-Oracle work. This is by no means specific to the Oracle Database or the Oracle applications. The AI Data Platform is agnostic and able to pull in and automate the ontologies from any data source. In fact, we have hundreds of data sources that we are doing this automation for today. So, whether it is just pure consumption of AI Data Platform, whether it is used in concert with our applications, or whether it is used in concert with OCI, we are more focused on allowing the customer to make the best choice, or the partner to make the best choice that suits them.

Oracle is already investing in forward-deployed engineers at its customers for a number of its AI products

We are already investing in deploying forward deployed engineers at our customers. That’s true for the AI Data Platform. It’s also true for our Fusion Agentic Studio, as we see them really as a combination platform running on a single control plane in OCI.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Adobe and MongoDB. Holdings are subject to change at any time.

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