Last month, I published More Of The Latest Thoughts From American Technology Companies On AI (2025 Q3). In it, I shared commentary in earnings conference calls for the third quarter of 2025, 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 2025’s third 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:

• 2023 Q1 – here and here
• 2023 Q2 – here and here
• 2023 Q3 – here and here
• 2023 Q4 – here and here
• 2024 Q1 – here and here
• 2024 Q2 – here and here
• 2024 Q3 – here and here
• 2024 Q4 – here, here, and here
• 2025 Q1 – here and here
• 2025 Q2 – here and here

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

Adobe (NASDAQ: ADBE)

Adobe’s nanagement has continued to develop the 1st-party Firefly models, while also expanding partnerships with other GenAI models; the new Firefly Image 5 model is performing really well; Firefly is the only app with Adobe’s own commercially safe models and over 25 leading partner models, including those from Google and OpenAI; monetisation of the usage of Adobe’s and 3rd-party models is through Generative Credits; different models and different media types consume different quantities of Generative Credits; consumption of Generative Credits increased 3x sequentially in 2025 Q3 (FY2025 Q4); subscribers who consume more Generative Credits can move to higher-value offerings or add credits; Adobe is attracting new creators through the Firefly application; management sees Firefly as a one-stop shop for accessing industry-leading models integrated into rich creative workflow at an affordable price; Creative Cloud customers are adopting Firefly, with 2x sequential growth in first-time subscriptions of Firefly in 2025 Q3 (FY2025 Q4); management has announced the general availability of Firefly Boards, a new ideation surface that is integrated with industry-leading models from both Adobe and 3rd parties; Firefly Services can now perform automated content production including video resizing, video reframing, image composition, image harmonization, digital-twin generation and more; more than 100 new deals for Firefly Services were signed in 2025 Q3 (FY2025 Q4) by enterprises

We have continued to develop our own commercially safe Firefly Models, while dramatically expanding our ecosystem of GenAI model partnerships. The new Firefly Image 5 model is performing incredibly well with generation quality, native 4- megapixel resolution and industry-leading prompt-based editing capabilities. At Adobe MAX in October, we significantly expanded Firefly to become the only app with our own commercially safe models and over 25 leading partner models including Google, OpenAI, Black Forest Labs, Luma, Runway, Topaz Labs and ElevenLabs. These models are now integrated into our Firefly, Express and Creative Cloud applications. We also announced advanced model capabilities including custom model support for Firefly and Creative Cloud customers. 

Usage and monetization of new Adobe and third-party models is measured and charged through Generative Credits. Different models (Firefly, Gemini or Flux, for example) and different media types (video and high-resolution images, for example), consume different quantities of Generative Credits. Generative Credits are a great indicator of high-value usage and credit consumption increased 3x quarter over quarter. As subscribers consume more generative credits, they have the choice of moving to higher value Creative Cloud offerings or acquiring Firefly Credit Add-ons…

…We are attracting new creators to Adobe through the Firefly application, which can be purchased through our Firefly Standard, Pro and Premium subscription plans. Firefly has a rich set of generative AI capabilities that allow users to generate with Adobe and partner models, ideate with Firefly Boards and create and edit videos and images. Simply put, Firefly is a one-stop shop for accessing industry-leading models integrated into rich creative workflows, at an affordable price.  In addition, we’re seeing strong adoption of Firefly from Creative Cloud customers, as they embrace the growing breadth of AI models and tools, seamlessly integrated into creative workflows. We drove 2x quarter-over-quarter growth in first-time subscriptions of Firefly…

…We also announced the general availability of Firefly Boards, a new ideation surface that brings together everything creative professionals need to explore visual and design concepts with stakeholders using industry-leading models, from Adobe and our partners…

…As part of the overall content supply chain solution for marketing use cases, we continue to advance automated content production with Firefly Services that include video resizing, video reframing, image composition, image harmonization, digital-twin generation and more…

…Accelerating adoption of Firefly Services within enterprises with over 100 new deals signed in Q4.

Adobe’s management has atomised Photoshop, Express and Acrobat capabilities as Model Context Protocol (MCP) endpoints; management sees LLMs (large language models) as a great top of funnel for customer acquisition for Adobe; the MCPs are important for Adobe because they allow LLMs to work with Adobe’s models and APIs, which helps Adobe reach more customers 

We also took a huge step forward in Q4 as we showcased the work we’ve been doing to atomize Photoshop, Express and Acrobat capabilities as Model Context Protocol (MCP) endpoints at Adobe MAX…

…Our focus has always been around sort of meeting customers where they are. And that used to predominantly be focused on search and the web, and now we’re seeing this incredible growth with LLMs. And so we are taking all of our technology and making sure that it can run in these LLMs. They represent, in our mind, a great fop of funnel. They let us reach new users that we typically wouldn’t have reached with some of the traditional markets that we go through, and we can engage them in new ways…

…Maybe the more important elements and moments of why this is such a critical moment for us is that as LLMs start embracing these model context protocols, these MCP endpoints, it’s no longer that these LLMs are about a prompt to a model and a response. It now gives us the opportunity to have the LLMs actually work with models and APIs, and that plays to a really strong strength that we have and durable differentiator given the incredible APIs we have across creativity and productivity. So it lets us reach a lot more customers, it lets us atomize the capabilities, double down on the freemium experience that we’ve been putting in place.

Adobe’s management is providing imaging, video, and productivity functionality in AI conversational platforms to monetize Adobe’s GenAI capabilities

In addition to delivering applications, we are providing imaging, video, and productivity functionality in ChatGPT, Copilot and other conversational platforms in order to deliver and monetize creative and PDF functionality in new surfaces. Users will be able to work conversationally while still benefiting from the power and precision of Adobe’s industry-leading features and direct-manipulation tools, making it easier than ever to go from intent to outcome, whether editing a PDF, refining an image, or generating a design.

Usage of AI features inside Acrobat and Reader is up 4x year-on-year in 2025 Q3 (FY2025 Q4); management introduced an AI Assistant into Adobe Express in 2025 Q3 (FY2025 Q4) that can generate content and perform complex editing; the AI Assistant has led to significant MAU (monthly active users) growth in Adobe Express; Adobe Acrobat Studio combines conversational, comprehension, and generative capabilities and the customer reception to Adobe Acrobat Studio has been strong

We revolutionized how users consume and comprehend documents by introducing Acrobat AI Assistant in FY24 and recently added PDF Spaces, allowing individuals and teams to create knowledge hubs to collaborate across multiple documents. Users package multiple documents – not just PDFs, but other file types and web links – into a single workspace they can share with others, enabling a collaborative conversational experience. Usage of these AI features inside Acrobat and Reader has grown more than 4x year over year, as users increasingly turn to Acrobat to help them discover insights, synthesize new ideas and share knowledge. 

Adobe Express made significant advances in Q4 with the introduction of an AI Assistant capable of generative content creation and complex editing. Express now supports generative presentations and designs, moving the industry into a post-template world. Express AI Assistant is capable of conversationally editing images, flyers, presentations, infographics and more. Innovations like these have contributed to significant Express MAU growth. 

Adobe Acrobat Studio brings together the conversational consumption and comprehension capabilities of AI Assistant and PDF Spaces with the generative creation power of Express, alongside the PDF tools people know and rely on into a unified offering. Customer reception of Acrobat Studio has been strong, with nearly 50% of Acrobat commercial ETLA’s renewed in Q4 already upgrading to this offering, reflecting user enthusiasm for unified document comprehension and content generation. 

Adobe’s management recently released Premier Mobile, a next-generation AI video editing tool; Adobe is partnering with Google and Youtube to introduce AI-driven audio and video tools to help creators remix YouTube Shorts

The release of Premiere Mobile in Q4 marks an important milestone in next-generation AI video editing. In partnership with Google and YouTube, we are introducing AI-driven audio and video tools to streamline how creators remix YouTube Shorts, which receive 200 billion daily views.

Creative Cloud recently released a number of new AI capabilities including new models for Generative Fill, and upscaling and prompt editing in Photoshop; management has announced the general availability of Firefly Boards, a new ideation surface that is integrated with industry-leading models from both Adobe and 3rd parties; usage of AI in Creative Cloud applications continues to accelerate; Generative Credit consumption in Creative Cloud, Firefly, and Express in the Creator & Creative Professional category is up 3x sequentially in 2025 Q3 (FY2025 Q4)

Creative Cloud delivered massive new value at Adobe MAX including the release of new models for Generative Fill, upscaling and prompt editing in Photoshop, reflection removal in Lightroom, Turntable in Illustrator and smart masking in Premiere. We also announced the general availability of Firefly Boards, a new ideation surface that brings together everything creative professionals need to explore visual and design concepts with stakeholders using industry-leading models, from Adobe and our partners. Use of AI in these applications continues to accelerate, underscoring the impact AI is having on what creative professionals can produce…

…[Creator & Creative Professional] Accelerating Generative Credit consumption in Creative Cloud, Firefly and Express by individuals and enterprises, which grew approximately 3x quarter over quarter

The MAU (monthly active users) of creative users across Firefly, Express, Premiere Mobile and other freemium offerings was up 35% year-on-year in 2025 Q3 (FY2026 Q4) to over 70 million

Growing our base of creative users across Firefly, Express, Premiere Mobile and other freemium offerings. MAU for these offerings surpassed 70 million in Q4, growing over 35% year over year.

Adobe’s management sees the Adobe Experience Platform (AEP) as a customer data platform that brings together new AI-powered apps and agents to drive customer engagement and loyalty, as well as reduce costs; AEP evaluates 35 trillion segments and activates 70 billion profiles daily; management has released 6 new AI agents powered by AEP Agent Orchestrator

Adobe Experience Platform (AEP) is a leading customer data platform that serves as the foundation in enterprises for digital customer engagement and brings together new AI-powered apps and agents to drive engagement and loyalty, as well as to reduce costs. Our platform operates at scale with over 35 trillion segment evaluations and more than 70 billion profile activations per day. We released six new AI agents powered by AEP Agent Orchestrator to transform how businesses build, deliver and optimize marketing campaigns and customer experiences.

Generative AI traffic to retail sites is up 760% in the 2025 holiday season; management is seeing AI-powered traffic to retail sites from LLMs and agentic browsers rising and this requires different approaches for conversion; the Adobe Experience Manager helps solves retailers’ needs in the agentic web; management thinks SemRush, which Adobe recently announced the acquisition of, has important assets that addresses marketers’ growing need for sustained brand relevance in AI search

Our most recent Adobe Digital Index data, which is based on online transactions across more than 1 trillion visits to U.S. retail sites, shows that generative AI traffic is up 760% thus far in the 2025 holiday season. Our data shows that AI-powered traffic from LLMs and agentic browsers is rising and requires different approaches to conversion, underscoring the growing importance of the agentic web and our opportunity to provide insights and automation to marketers.

Brand visibility is critical to success in this new agentic web, and Adobe solves customer needs through solutions like Adobe Experience Manager, Adobe Analytics and the newly available Adobe LLM Optimizer. The pending acquisition of Semrush, which we announced a few weeks ago, brings complementary assets to help us address marketers’ growing need for sustained brand relevance in AI search. Over the past decade, Semrush’s data-driven search engine optimization and generative engine optimization solutions have earned the trust of industry leaders like Amazon, JPMorganChase and TikTok. Together, Adobe and Semrush will deliver a comprehensive solution to enable marketers to shape how their brands appear across owned channels, LLMs, traditional search and the wider web.

Adobe’s management launched Adobe Brand Concierge in 2025 Q3 (FY2025 Q4), an AI-first application for businesses to manage AI agents for agentic commerce; management is seeing significant customer interest in Adobe Brand Concierge

Adobe Brand Concierge, which was launched in Q4, is an AI-first application enabling businesses to configure and manage AI agents that guide consumers from exploration to purchase decisions, using immersive and conversational experiences. By uniting data, content and agentic AI in a single experience, Brand Concierge gives businesses ownership of the critical discovery and consideration phase. We’re pleased with the significant customer interest and the wins we had for Brand Concierge in Q4.

Adobe GenStudio’s ending ARR grew 25% year-on-year in 2025 Q3 (FY2025 Q4); management sees GenStudio as the product that takes care of every aspect of content production for enterprises

GenStudio is our comprehensive offering spanning content ideation, creation, production, and activation. At MAX, we introduced new scaled content production capabilities through Firefly Services, enhanced model customization with Adobe Firefly Foundry, and integration with a growing ecosystem of ad networks. Ending ARR for the Adobe GenStudio solution grew over 25% year over year as the world’s leading brands increasingly turn to Adobe to power their content supply chain…

…GenStudio is really the offering that we want to provide that takes care of every aspect of their content production, whether it’s the creation part of the campaign, whether it’s then creating custom models, whether it’s training it at the back end, whether it’s automating it and then certainly delivery

Adobe’s new agentic web offerings, Adobe LLM Optimizer, Sites Optimizer, and Brand Concierge, had over 50 customers in 2025 Q3 (FY2025 Q4)

Strong customer demand for our newly introduced agentic web offerings with over 50 customers in Q4 for Adobe LLM Optimizer, Sites Optimizer, and Brand Concierge

Adobe’s new AI-influenced ARR is now more than one-third of overall business, or more than $2 billion

Total new AI-influenced ARR now exceeds one-third of our overall business as we integrate AI deeply into our solutions and continue to launch new AI-first offerings which are now included as part of the AI-influenced metric.

Adobe’s management has announced Adobe Firefly Foundry, which provides enterprises with proprietary foundation models trained on their own intellectual property; interest in Firefly Foundry is strong; Firefly Foundry is operated as a managed service; a media and entertainment company that’s an existing customer of Adobe with $10 million of ARR signed for Firefly Services and Firefly Foundry for an additional $7 million; the media and entertainment company was able to train its proprietary model in 2-3 months, and is already seeing increased efficiency in content production; management’s vision for Firefly Foundry is to have a Foundry be created specifically for every single franchise

We also announced Adobe Firefly Foundry at MAX, which delivers enterprises with proprietary foundation models trained on their own content, data and brand catalogs. Interest in Firefly Foundry has been strong from enterprise marketing teams and media and entertainment companies, where there is increasing desire to produce content faster and more cost-effectively…

…We introduced Foundry, like you mentioned, at MAX. And the core value is that we train on their content, their data and their brand guidelines. We’re able to generate images, videos, audio and 3D models and we operate it as a managed service. So marketing teams can then train on product shots, environment styles, brand guidelines and media companies actually train on their individual franchises, whether it’s a movie or whether it’s a series, they’ll train their characters, their sets, their props, their locations so they can generate the whole thing…

…Let’s take a media and entertainment company we’re working on, and I’m rounding the numbers here, but to give you a little bit of context, let’s say, that organization was spending $10 million with us ARR on our core creative products that we’ve been selling with them. We ran a sales process with them, engagement with them for about 6 months. We were able to sell them Firefly Services and Firefly Foundry for about $7 million, so a pretty significant step up in terms of the engagement that we have with the customer. We were able to train models within 2 or 3 months, and now we’re running some of those models specifically as managed services for them for ideation and production processes. They’re already seeing increased efficiency in content production. They’re able to generate more production content, and they’re now getting into opportunities that are revenue-bearing opportunities like increasing the types of content they produce for social shorts and personalizing more of it for fan engagement with integration with our real-time CDP…

…The vision clearly is that for every single brand, if you’re a consumer company or for every single TV show or a movie, we can create a Foundry specifically for that particular franchise, as David said, because the ability to help with the automation of that content and production is massive.

Adobe’s management sees Adobe as the only company that can close the loop from the creation of an AI-powered advertising campaign, the execution of that campaign, to the commerce impact of the campaign

There are trillions of dollars that are spent in marketing and our opportunity is to really say we can help you make sure that, that content is more personalized. I’ll have Anil also add after this and deliver it. And the fact is that since we can deliver that content through an ad network and then we understand through our analytics where that is resulting in traffic, where that is resulting in conversion, where that’s not, we’re the only company that can close the loop from the creation of a campaign, the execution of that campaign as well as then actually looking at what that causes in terms of commerce. And so I think our real value proposition in all of this is that as increasingly people are saying, “Hey, I want to use AI to create more.” We can not only optimize and accelerate the amount of content that they’re producing, but we’re the only company that can then help them say, “Hey, this caused so much traffic.”

MongoDB (NASDAQ: MDB)

MongoDB’s management thinks that the AI wave has yet to meaningfully impact MongoDB’s results; it’s still early days, but management is already seeing AI startups building applications on MongoDB; management is seeing large enterprises develop AI agents on MongoDB, but these agents are pilot projects and there are currently no AI agents running in production that can fundamentally transform businesses or serve customers better; management thinks there’s a lot of work needed to change an AI application prototype into one that is enterprise-ready; management is seeing that industries that are regulated have very different requirements for an AI agent to be in production compared to being in prototype; management is seeing enterprises try out and churning from many different AI coding agents

All of this momentum in the core business is happening before the AI wave has meaningfully impacted our results. We are still early, but the signs are encouraging from AI-native start-ups building intelligent applications on MongoDB to large enterprises developing AI agents that will reshape how they operate…

…There are various co-pilots when it comes to productivity types of applications that are happening inside of an organization, whether it’s a bank or a health care organization or a manufacturing organization. But what I have not seen is truly AI agents running in production that fundamentally transform the business or serve customers better. There are many, many pilots still going on…

…We’re clearly seeing a lot of, I would say, prototyping and iteration. I would say the enterprise requirements still have a pretty strong and stringent requirements around security and durability and performance. So while there’s a big difference between coming out with the prototype and having a production-grade system that an enterprise can truly rely on trust. And so there is still a lot of work required to make those applications enterprise class…

…When I speak to customers who I’ve been speaking for a long time, in regulated industries, which is financial services, which is health care, which is public sector, the requirement for an AI agent to be in production versus prototype are vastly different, and they are looking for governance, auditability, this and that, while the innovation and the need for the speed is very high. So I have not seen — like customers will tell me, CJ I have 10 agents in production, 15 agents in production. And when I really asked them, I say, are they really customer-facing? Can they be audited on the probabilistic outcome they derive? The answer is, oh, we are still working through that…

…Even the environment on which they are building agents, they are telling me they try one, it doesn’t work, they move on to the next one. So the churn for some of these AI companies that deliver these tools is also very real.

MongoDB’s management thinks that AI applications must connect LLMs (large language models) with companies’ proprietary data, and this connection is an information retrieval problem that requires a very different architecture from the rigid tabular stores that traditional software depended on; management thinks that MongoDB’s document database model has a structural advantage with the architecture that AI applications require; the Voyage MongoDB models is #1 on the Hugging Face retrieval embedding benchmark; MongoDB’s database is the #1 vector database on DB-Engines; MongoDB’s improvement of its embedding and reranking models have driven meaningful accuracy gains and lowered LLM hallucinations; management is hearing from AI native companies that alternatives to MongoDB and relational databases do not scale for AI workloads 

AI applications must connect what LLMs know with what companies know, which is their proprietary data, systems and real-time context. This is fundamentally an information retrieval problem, and it requires a very different architecture than the last generation of software. Rapidly evolving AI models uncover new complex properties about entities and rigid tabular stores cannot deliver the real-time high accuracy performance that AI systems require. At the same time, AI is dramatically increasing the speed at which applications are built and iterated and fixed database schemas simply cannot keep pace.

This is where MongoDB has a structural advantage. Our document model, natively, JSON is built for diverse class changing and interdependent data. Our integrated search, vector search and Voyage embeddings removed the need for brittle bolt-ons, and we are seeing industry-leading results. Number one on the Hugging Face retrieval embedding benchmark with Voyage MongoDB models and the #1 vector database on DB-Engines. Advances in our embedding and reranking models drive meaningful accuracy gains, enabling AI applications to deliver more grounded responses with fewer LLM hallucinations, while lowering storage cost and query cost through smaller, more efficient embeddings…

…Speaking to my network in Silicon Valley with AI-native companies or digital-native companies, what I hear from them is that certain alternatives on relational database just do not scale because AI workloads are fundamentally around unstructured and semi-structured data.

The AI-powered hiring startup Mercor is using MongoDB Atlas to store AI data behind its platform that directly connects professionals to AI model training and evaluation roles; Mercor is also using Voyage embeddings and Atlas Vector Search; MongoDB Atlas is able to support Mercor’s 50% monthly growth, whereas Mercor’s previous solution, Postgres, was not able to

Mercor, which is redefining hiring with its fully automated platform that uses AI to assess and match talent with the opportunities they are best suited for. Mercor uses MongoDB Atlas to store the AI data behind its platform that directly connects professionals to AI model training and evaluation roles. Originally, a self-serve customer, the company is also utilizing Voyage embeddings and Atlas Vector Search. Atlas has scale to support Mercor’s 50% month-over-month growth, allowing the company to keep its software engineering team lean and agile as it expands to over $10 billion in value…

…In my remarks, I shared that there is a super high growth AI company that is doing very, very well and will become a very large company. I have absolutely no doubts about that. They were not able to scale with Postgres and few other technologies, Redis and so on that they were using, and they moved completely to MongoDB and seeing that week-over-week and month-over-month growth is super inspiring. And I spoke to the hyperscaler where this workload is running and they are seeing the same that, wow, this company is doing really well. So that’s built on MongoDB because Postgres had scaling issue.

A global media company running multi-modal content recommendation workloads switched from Elastic Search to MongoDB Atlas and MongoDB Atlas Search after hitting a performance wall with Elastic Search; the media company was able to integrate Voyage AI models in just weeks; MongoDB has helped the media company to cut latency by 90%, reduce operational spend by 65%, and increase click-through rates by 35% 

A highly influential global media company aim to increase engagement via enhanced content recommendation for its vast repository of multimodal assets across its 70-plus websites. That existing stack powered by Elasticsearch hit a performance wall struggling with the complexity of new embedding models. Recognizing that [ rigid ] systems stifle innovation, the engineering team re-architected on MongoDB Atlas and MongoDB Atlas Vector search. Working with MongoDB experts to deliver a proof of concept in just weeks, they integrated Voyage AI models directly alongside their data. The solution scale effortlessly, cutting latency by 90% and reducing operational spend by 65% and driving a 35% increase in click-through rates, ultimately providing millions of global readers with a seamless, deeply personalized discovery journey.

MongoDB’s management is seeing the multi-cloud or public cloud transformation trend continue to happen, and will do so for the next 5-7 years; management thinks it’s possible that the emergence of AI is driving higher demand for application modernisation

The modernization effort, whether it’s a workload that may be just running on-prem, in a large enterprise or a workload that is moving to cloud or sometimes to multiple clouds for resiliency that transformation in speaking to a large telecommunications company, a large health care company, a large tech company, and I can cite you many other examples. I was pretty overwhelmed to understand that those transformations are still going on. There is just a recent conversation I had with CTO of a large telecommunications company who said that they are moving 1,300-plus applications to another hyperscaler and trying to determine which workloads are best suited for MongoDB. So the whole multi-cloud or a public cloud transformation is still going on. And just my intuitive sense in speaking to these customers will be going on for at least next 5 to 7 years…

…This is my personal experience in building AI technologies in the past. That the AI team is typically a separate team from the core data team. And AI team relies on the core data team. And if the core data team moves slow, then AI teams get really frustrated because innovation velocity is how they measure themselves on. So my personal experience was, hey, when the core team is not agile there schemas are not flexible, it actually slows AI down. So that is definitely some facts behind your theory that it is potentially the AI revolution, which we are still in the early stages, is driving modernization in the other part of the enterprise.

A fast-growing AI startup that built its own vector database decided to give Voyage’s AI embedding models a try; if the startup sees good results with Voyage, it will switch from its in-house vector database to MongoDB; a very large customer of MongoDB has deep appreciation for Voyage AI embedding models and are already running 2 big workloads on it

I spoke to a fairly successful AI native company that is doing decent ARR, growing very fast. And when I said, hey, have you considered MongoDB to the founder, CEO, who is very technical. And he said, CJ, we didn’t, we built our own vector database and so on. And while I was speaking to him Alex, about 10 days ago, he basically said, once he looked at the portfolio, he said, let me start with embeddings first. So we are going to try. Of course, we have to prove it to him why our embeddings improves his accuracy on search and so on and improve the performance. So he said, let’s start with embedding models first from Voyage AI once that works CJ, I’m willing to replace my vector DB that we have homegrown created it with MongoDB and oh, by the way, if that works well, eventually, I’m willing to swap out my operational database as well and use MongoDB…

…I’m also seeing in a very large customer of MongoDB, I spoke to somebody who is running the AI initiatives, and they love the Voyage AI embeddings and reranking model, and they’ve already approved it for 2 big workloads.

MongoDB’s management sees AI coding tools as a tailwind for MongoDB because it increases the pace of software creation, and hence, drives demand for databases

Clearly, with the advent of co-gen tools, the rate and pace of software development is only going to increase. And as I think we said in the past, that’s one of the big reasons why we think AI is a tailwind. It’s just that the ability to produce more software, more database, and more and more strategies has been encapsulated in software. So from that point of view, we think that’s all good news for us.

NVIDIA (NASDAQ: NVDA)

NVIDIA’s management is seeing AI going everywhere, doing everything, all at once

AI is going everywhere, doing everything, all at once.

NVIDIA’s management is seeing off the charts demand for Blackwell; management has visibility to $0.5 trillion of revenue for its Blackwell and Rubin platforms from the start of 2025 through to 2026, with about $0.34 trillion over the next 14 months; management sees opportunities for Blackwell and Rubin to have more than $0.5 trillion of revenue from the start of 2025 through to 2026

Blackwell sales are off the charts, and cloud GPUs are sold out…

…We currently have visibility to $0.5 trillion in Blackwell and Rubin revenue from the start of this year through the end of calendar year 2026…

…[Question] You talked about the $500 billion of revenue for Blackwell plus Rubin in ’25 and ’26 at GTC. At that time, you talked about $150 billion of that already having been shipped. So as the quarter is wrapped up, are those still kind of the general parameters that there’s $350 billion in the next kind of 14 months or so?

[Answer] Yes, that’s correct. We are working into our $500 billion forecast. And we are on track for that as we have finished some of the quarters, and now we have several quarters now in front of us to take us through the end of calendar year ’26. The number will grow. And we will achieve, I’m sure, additional needs for compute that will be shippable by fiscal year ’26. So we shipped $50 billion this quarter, but we would be not finished if we didn’t say that we’ll probably be taking more orders… There’s definitely an opportunity for us to have more on top of the $500 billion that we announced.

NVIDIA’s management sees $3 trillion to $4 trillion of annual AI infrastructure build by 2030, with NVIDIA’s platforms being the superior choice; demand for AI infrastructure continues to exceed management’s expectations; management thinks the hyperscalers’ workload transitions would be half of the company’s long-term opportunity; management thinks the other half of NVIDIA’s long-term opportunity would come from higher compute spend by foundation model builders; the dollar-content of NVIDIA chips in AI data centers has been increasing with each successive generation

By executing our annual product cadence and extending our performance leadership through full stack design, we believe NVIDIA will be the superior choice for the $3 trillion to $4 trillion in annual AI infrastructure build we estimate by the end of the decade. Demand for AI infrastructure continues to exceed our expectations…

…We see the transition to accelerate computing and generative AI across current hyper workloads contributing toward roughly half of our long-term opportunity. Another growth pillar is the ongoing increase in compute spend driven by foundation model builders such as Anthropic, Mistral, OpenAI, Reflection, Safe Superintelligence, Thinking Machines Lab and xAI, all scaling, compute aggressively to scale intelligence…

…[Question] What assumptions are you making on NVIDIA content per gigawatt in that $500 billion number? Because we have heard numbers as low as $25 billion per gigawatt of content to as high as $30 billion or $40 billion per gigawatt. So I’m curious what power and what dollar per gig assumptions you are making as part of that $500 billion number.

[Answer] In each generation, from Ampere to Hopper, from Hopper to Blackwell, Blackwell to Rubin, our part of the data center increases. And Hopper generation was probably something along the lines of 20-some-odd, 20 to 25. Blackwell generation, Grace Blackwell particularly is probably 30 to 30 to say, 30 plus or minus and then Rubin is probably higher than that.

The installed base of NVIDIA GPUs, including the older generation Hopper and Ampere families, are fully utilised; NVIDIA’s GPUs have long useful lives, which gives them a significant TCO (total cost of ownership) advantage over competing chips; the long useful lives of NVIDIA’s GPUs is the result of the company’s CUDA software stack; NVIDIA’s 6-year-old A100 GPUs are still fully utilised today

Our GPU installed base, both new and previous generations, including Blackwell, Hopper and Ampere is fully utilized…

…The long useful life of NVIDIA’s CUDA GPUs is a significant TCO advantage over accelerators. CUDA’s compatibility in our massive installed base, extend the life NVIDIA Systems well beyond their original estimated useful life…

…Most accelerators without CUDA and NVIDIA’s time-tested and versatile architecture became obsolete within a few years as model technologies evolve. Thanks to CUDA, the A100 GPUs we shipped 6 years ago are still running at full utilization today, powered by vastly improved software stack.

NVIDIA’s Data Center revenue again had very strong growth in 2025 Q3 (FY2026 Q3), driven partly by the GB300 chip from the Blackwell family; GB300 was 2/3 of total Blackwell revenue in 2025 Q3 (FY2026 Q3); the Blackwell Ultra chip delivers 5x faster time to train than Hopper; Blackwell had the highest performance and lowest total cost of ownership across every model and use case under the InferenceMAX benchmark; Blackwell delivers a 10x higher performance per watt and 10x lower cost per token compared to H200 on the Deepseek-R1 model; TSMC delivered the first US-produced Blackwell chip in October 2025; 

Record Q3 data center revenue of $51 billion (sic) [ $51.2 billion ] increased 66% year-over-year, a significant feat at our scale. Compute grew 56% year-over-year, driven primarily by the GB300 ramp, while networking more than doubled, given the onset of NVLink scale up and robust double-digit growth across Spectrum-X Ethernet and Quantum-X InfiniBand…

…GB300 crossed over GB200 and contributed roughly 2/3 of the total Blackwell revenue. The transition to GB300 has been seamless with production shipments to the majority — to the major cloud service providers, hyperscalers and [ GP clouds ] and is already driving their growth…

…In the latest MLPerf training results, Blackwell Ultra delivered 5x faster time to train than Hopper. NVIDIA swept every benchmark. Notably, NVIDIA is the only training platform to leverage FP4 while meeting the MLPerf’s strict accuracy standards. In Semi Analysis’s, InferenceMAX benchmark, Blackwell achieved the highest performance and lowest total cost of ownership across every model and use case. Particularly important is Blackwell’s NVLinks performance on a mixture of experts, the architecture for the world’s most popular reasoning models. On DeepSeek-R1 Blackwell delivered 10x higher performance per watt and 10x lower cost per token versus H200, a huge generational leap fueled by our extreme co-design approach…

…Last month, in partnership with TSMC, we celebrated the first Blackwell wafer produced on U.S. soil.

NVIDIA’s management is seeing the hyperscalers transitioning their workloads from classical machine learning to generative AI; management thinks NVIDIA’s CUDA software stack excels at both classical machine learning and generative AI; management is seeing the hyperscalers’ capex-expectations for 2026 increase by $200 billion since the start of the year to $600 billion; management thinks the hyperscalers’ workload transitions would be half of the company’s long-term opportunity

The world hyperscalers, a trillion-dollar industry are transforming search, recommendations and content understanding from classical machine learning to generative AI. NVIDIA CUDA excels at both and is the ideal platform for this transition, driving infrastructure investment measured in hundreds of billions of dollars…

…Expectations for the top CSPs and hyperscalers in 2026, aggregate CapEx have continued to increase and now sit roughly at $600 billion, more than $200 billion higher relative to the start of the year…

…We see the transition to accelerate computing and generative AI across current hyper workloads contributing toward roughly half of our long-term opportunity.

NVIDIA’s management is seeing Meta Platforms increase users’ time spent on Facebook and Threads because its AI recommendation systems are showing up better content; when Meta Platforms’ generative AI foundation model, GEM, drove a 5% increase in ad conversions on Instagram and 3% gain on Facebook feed in 2025 Q2

At Meta, AI recommendation systems are delivering higher quality and more relevant content, leading to more time spent on apps such as Facebook and Threads…

…Meta’s GEM, a foundation model for ad recommendations trained on large-scale GPU clusters exemplifies this shift. In Q2, Meta reported over a 5% increase in ad conversions on Instagram and 3% gain on Facebook feed driven by generative AI-based GEM. Transitioning to generative AI represents substantial revenue gains for hyperscalers.

NVIDIA’s management is seeing the 3 scaling laws of pre-training, post-training, and inference, to be intact

The 3 scaling laws, pretraining post training and inference remain intact. In fact, we see a positive virtuous cycle emerging whereby the 3 scaling laws and access to compute are generating better intelligence and in turn, increasing adoption and profits…

…Just today, I was reading a text from Demis. And he was saying that pre-training and post training are fully intact. And Gemini 3 takes advantage of the scaling laws and got to receive a huge jump in quality performance — model performance.

NVIDIA’s management is observing a proliferation of AI agents; RBC is using agentic AI to reduce report generation time from hours to minutes

We are also witnessing a proliferation of agentic AI across various industries and tasks. Companies such as Cursor, Anthropic, OpenEvidence, Epic and Abridge are experiencing a surge in user growth as they supercharge the existing workforce, delivering unquestionable ROI for coders and health care professionals…

…RBC is leveraging agentic AI to drive significant analyst productivity, slashing report generation time from hours to minutes.

NVIDIA’s management continues to engage the US and China governments on the sale of American chips into China

While we were disappointed in the current state that prevents us from shipping more competitive data center compute products to China, we are committed to continued engagement with the U.S. and China governments and will continue to advocate for America’s ability to compete around the world. To establish a sustainable leadership position in AI computing, America must win the support of every developer and be the platform of choice for every commercial business, including those in China.

NVIDIA’s next generation of chips, the Rubin family, are on track for volume production in 2026 H2; 7 different chips go into the Vera Rubin platform; management sees Rubin delivering much better performance than Blackwell; Rubin’s manufacturing is compatible with Blackwell, and the manufacturing ecosystem is ready to ramp Rubin

The Rubin platform is on track to ramp in the second half of 2026. Powered by 7 chips, the Vera Rubin platform will once again deliver an X-factor improvement in performance relative to Blackwell…

…Rubin is our third-generation rack-scale system substantially redefined the manufacturability while remaining compatible with Grace Blackwell. Our supply chain data center ecosystem and cloud partners have now mastered the build to installation process of NVIDIA’s rack architecture. Our ecosystem will be ready for a fast Rubin ramp.

NVIDIA’s networking revenue had very strong sequential as well as year-on-year growth in 2025 Q3 (FY2026 Q3), driven by strong demand across Spectrum-X Ethernet, InfiniBand and NVLink (networking revenue was $7.3 billion in 2025 Q2); the majority of AI deployments now include NVIDIA networking switches; NVIDIA Ethernet attach rates are now roughly on par with Infiniband; major AI players are building gigawatt AI data centers with Spectrum-X Ethernet; management recently introduced Spectrum-XGS, a scale across technology; NVIDIA is the only company with AI networking solutions for scale up, scale out, and scale across; NVIDIA recently announced a collaboration to link Fujitsu’s CPUs and NVIDIA GPUs via NVLink Fusion; NVIDIA has a partnership with Intel to connect Intel’s CPUs and NVIDIA GPUs with NVLink; Arm recently announced that it will be using NVLink IP for customers to connect Arm CPU designs with NVIDIA’s platforms; management sees NVLink as the only proven scale up networking solution in the market today

Our networking business purpose built for AI and now the largest in the world, generated revenue of $8.2 billion, up 162% year-over-year with NVLink, InfiniBand and Spectrum-X Ethernet, all contributing to growth. We are winning in data center networking, as the majority of AI deployments now include our switches with Ethernet GPU attach rates roughly on par with InfiniBand. Meta, Microsoft, Oracle and xAI are building gigawatt AI factories with Spectrum-X Ethernet switches and each will run its operating system of choice, highlighting the flexibility and openness of our platform.

We recently introduced Spectrum-XGS, a scale across technology that enables gigascale AI factories. NVIDIA is the only company with AI scale up, scale out and scale across platforms, reinforcing our unique position in the market as the AI infrastructure provider.

Customer interest in NVLink Fusion continues to grow. We announced a strategic collaboration with Fujitsu in October, where we will integrate Fujitsu’s CPUs and NVIDIA GPUs via and NVLink Fusion, connecting our large ecosystems. We also announced a collaboration with Intel to develop multiple generations of custom data center and PC products, connecting NVIDIA and Intel’s ecosystems using NVLink. This week at Supercomputing ’25, Arm announced that it will be integrating NVLink IP for customers to build CPU SoCs that connect with NVIDIA. Currently on its fifth generation, NVLink is the only proven scale up technology available on the market today.

NVIDIA’s open source inference framework, NVIDIA Dynamo, has now been adopted by every major cloud service provider

NVIDIA Dynamo, an open source, low latency modular inference framework has now been adopted by every major cloud service provider, leveraging Dynamo’s enablement and disaggregated inference. The resulting increase in performance of complex AI models, such as MoE models, AWS, Google Cloud, Microsoft Azure and OCI have boosted AI inference performance for enterprise cloud customers.

NVIDIA’s management is working on a strategic partnership with OpenAI to deploy AI data centers and for NVIDIA to invest in OpenAI; NVIDIA is serving OpenAI through Microsoft Azure, Oracle Cloud Infrastructure (OCI), and CoreWeave and will continue to do so in the future; management is happy to support OpenAI’s self-build AI infrastructure; management recently inked a partnership with Anthropic that will see Anthropic use NVIDIA for the first time; management will optimise Anthropic’s models for CUDA, and optimise future NVIDIA chips for Anthropic workloads; Anthropic’s initial commitment to NVIDIA is for up to 1 gigawatt of compute capacity; the investments NVIDIA has been making in the AI ecosystem is to expand the reach of CUDA; management expects NVIDIA’s investment in OpenAi to generate extraordinary returns

We are working on a strategic partnership with OpenAI, focused on helping them build and deploy at least 10 gigawatts of AI data centers. In addition, we have the opportunity to invest in the company. We serve OpenAI through their cloud partners, Microsoft Azure, OCI and CoreWeave. We will continue to do so for the foreseeable future. As they continue to scale, we are delighted to support the company to add self-build infrastructure, and we are working towards a definitive agreement and are excited to support OpenAI’s growth.

Yesterday, we celebrated an announcement with Anthropic. For the first time, Anthropic is adopting NVIDIA, and we are establishing a deep technology partnership to support Anthropic’s fast growth. We will collaborate to optimize Anthropic models for CUDA and deliver the best possible performance, efficiency and TCO. We will also optimize future NVIDIA architectures for Anthropic workloads. Anthropic’s compute commitment is initially including up to 1 gigawatt of compute capacity with Grace Blackwell and Vera Rubin Systems…

…All of the investments that we’ve done so far, all the period, is associated with expanding the reach of CUDA expanding the ecosystem…

…That relationship we’ve had since 2016, I delivered the first AI supercomputer ever made to OpenAI. And so we’ve had a close and wonderful relationship with OpenAI since then. And everything that OpenAI does runs on NVIDIA today. So all the clouds that they deploy in, whether it’s training and inference runs NVIDIA and we love working with them. The partnership that we have with them is one, so that we could work even deeper from a technical perspective so that we could support their accelerated growth. This is a company that’s growing incredibly fast. And don’t just look at what is said in the press, look at all the ecosystem partners and all the developers that are connected to OpenAI, and they’re all driving consumption of it. and the quality of the AI that’s being produced, huge step-up since a year ago. And so the quality of response is extraordinary. So we invest in OpenAI for a deep partnership in co-development to expand our ecosystem and support their growth. And of course, rather than giving up a share of our company, we get a share of their company. And we invested in them, in one of the most consequential once-in-a-generation companies that we have a share of. And so I fully expect that investment to translate to extraordinary returns.

NVIDIA’s management sees physical AI as a multi-trillion dollar opportunity; physical AI is already a multi-billion business for NVIDIA; leading US robotics companies are using NVIDIA’s products, including Omniverse; many enterprises, including TSMC, are building Omniverse digital twin factories; robotics companies, including Amazon Robotics, are using NVIDIA Cosmos World Foundation Models, Omniverse, and Jetson, to develop their robots

Physical AI is already a multibillion-dollar business addressing a multitrillion dollar opportunity on the next leg of growth for NVIDIA. Leading U.S. manufacturers and robotics innovators are leveraging NVIDIA’s 3 computer architecture to train on NVIDIA, test on Omniverse’s computer and deploy real-world AI. And just in robotic computers, PTC and Siemens introduced new services that bring Omniverse powered digital twin workflows to their extensive installed base of customers. Companies, including Belden, Caterpillar, Foxconn, Lucid Motors, Toyota, TSMC and Wistron are building Omniverse digital twin factories to accelerate AI-driven manufacturing and automation. Agility Robotics, Amazon Robotics, Figure and Skild at AI are building our platform, tapping offerings such as NVIDIA Cosmos World Foundation Models for development, Omniverse for simulation and validation and Jetson to power next-generation intelligent robots.

NVIDIA is partnering with Uber for the world’s largest Level 4 ready autonomous fleet

We are partnering with Uber to scale the world’s largest Level 4 ready autonomous fleet built on the new NVIDIA Hyperion L4 robotaxi reference architecture.

NVIDIA’s management is not seeing an AI bubble; management sees 3 computing transformations happening in the world simultaneously and NVIDIA is addressing all of them; the 3 transformations are (1) the transition from CPUs to GPUs, (2) transformation of existing applications by AI, and (3) AI agents

There’s been a lot of talk about an AI bubble. From our vantage point, we see something very different…

…The world is going — is undergoing 3 massive platform shifts at once. The first time since the dawn of Moore’s Law, NVIDIA is uniquely addressing each of the 3 transformations.

The first transition is from CPU general purpose computing to GPU accelerated computing and Moore’s Law slows. The world has a massive investment in non-AI software from data processing to science and engineering simulations, representing hundreds of billions of dollars in compute — cloud computing spend each year. Many of these applications, which ran once exclusively on CPUs are now rapidly shifting to CUDA GPUs. Accelerated computing has reached a tipping point. 

Secondly, AI has also reached a tipping point and is transforming existing applications while enabling entirely new ones. For existing applications, generative AI is replacing classical machine learning in search ranking, recommender systems, ad targeting, click-through prediction to content moderation. The very foundations of hyperscale infrastructure… 

…Now a new wave is rising, agentic AI systems capable of reasoning, planning and using tools from coding assistance like Cursor and Claude Code to radiology tools like Aidoc, legal assistants like Harvey and AI chauffeurs like Tesla FSD and Waymo.  

NVIDIA’s management thinks the company excels at each phase of AI, from pre-training to inference

NVIDIA is unlike any other accelerator. We excel at every phase of AI from pre-training and post training to inference.

The pioneers of agentic AI that management is seeing are all startups

These systems mark the next frontier of computing, the fastest-growing companies in the world today, OpenAI, Anthropic, xAI, Google, Cursor, Lovable, Replit, Cognition AI, OpenEvidence, Abridge, Tesla are pioneering agentic AI.

The fastest-growing applications in history are AI-powered coding applications

The fastest-growing application in history, a combination of Cursor and Claude Code and code — OpenAI’s Codex and GitHub CoPilot. These applications are the fastest-growing in history. And it’s not just used for software engineers, it’s used by — because of wide coding is used by engineers and marketeers all over companies, supply chain planners, all over companies.

NVIDIA’s platform is the only one in the world that runs every AI model

NVIDIA’s architecture, NVIDIA’s platform is the singular platform in the world that runs every AI model. We run OpenAI, we run Anthropic, we run xAI because of our deep partnership with Elon and xAI, we were able to bring that opportunity to Saudi Arabia to the KSA so that HUMAIN could also be hosting opportunity for xAI. We run xAI, we run Gemini, we run Thinking Machines, let’s see, what else do we run? We’ve run them all. And so not to mention, we run the science models, the biology models, DNA models, gene models, chemical models and all the different fields around the world. It’s not just cognitive AI that the world uses, AI is impacting every single industry.

NVIDIA’s management hopes inference will become a large portion of the use case for NVIDIA GPUs because that will suggest that people are using AI in more applications

[Question] In the past, you’ve talked about roughly 40% of your shipments tied to AI inference. I’m wondering, as you look forward into next year, where do you expect that percentage could go in, say, a year’s time?

[Answer] Inference because of chain of thought, because of reasoning capabilities, AIs are essentially reading, thinking before it answers. And the amount of computation necessary as a result of those 3 things has gone completely exponential. I think that it’s hard to know exactly what the percentage of it will be at any given point in time and who. But of course, our hope is that inference is a very large part of the market because if inference is large, then what it suggests is that people are using it in more applications and they’re using it more frequently. And that’s — we should all hope for inference to be very large.

NVIDIA’s management sees a number of important constraints on the growth of the AI ecosystem, namely, power and financing, but they are all solvable problems 

[Question] Many of your customers are pursuing behind-the-meter power, but like what’s the single biggest bottleneck that worries you that could constrain your growth? Is it power? Or maybe it’s financing or maybe it’s something else like memory or even foundry?

[Answer] These are all issues and they’re all constraints. And the reason for that, when you’re growing at the rate that we are and the scale that we are, how could anything be easy?… Now on the one hand, we are transitioning computing from general purpose and classical or traditional computing to accelerated computing and AI. That’s on the one hand. On the other hand, we created a whole new industry called AI factories. The idea that in order for software to run, you need these factories to generate it, generate every single token instead of retrieving information that was pre-created. And so I think this whole transition requires extraordinary scale. And all the way from the supply chain. Of course, the supply chain, we have much better visibility and control over because obviously, we’re incredibly good at managing our supply chain. We have great partners that we’ve worked with for 33 years. And so the supply chain part of it, we’re quite confident. Now looking down our supply chain, we’ve now established partnerships with so many players in land and power and shell. And of course, financing. These things — none of these things are easy, but they’re all attractable and they’re all solvable things.

NVIDIA’s management thinks it’s incredibly hard for ASICs (application specific integrated circuits) for AI workloads to compete against NVIDIA GPUs because NVIDIA’s GPU systems (1) are now incredibly complex and (2) can run every AI model

[Question] I’m curious if your thoughts around the role that AI ASICs or dedicated XPUs play in these architecture build-outs has changed at all? Have you seen, I think you’ve been fairly adamant in the past that some of these programs never really see deployments. But I’m curious if we’re at a point where maybe that’s even changed more in favor of just GPU architecture.

[Answer] Back in the Hopper day and the Ampere days, we would build one GPU. That’s the definition of an accelerated AI system. But today, we’ve got to build entire racks entire — 3 different types of switches, scale up, scale out and scale across switch. And it takes a lot more than 1 chip to build a compute node anymore. Everything about that computing system because AI needs to have memory, AI didn’t use to have memory at all. Now it has to remember things, the amount of memory and context it has is gigantic. The memory architecture implication is incredible. The diversity of models from mixture of experts to dense models, to diffusion models that are aggressive not to mention biological models that are based on the laws of physics, the list of different types of models have exploded in the last several years. And so the challenge is the complexity of the problem is much higher…

…We’re now the only architecture in the world that runs every AI model, every frontier AI model, we run open source AI models incredibly well. We run science models, biology models, robotics models. We run every single model. We’re the only architecture in the world that can claim that. It doesn’t matter whether you’re auto regressive or diffusion based. We run everything and we run it for every major platform, as I just mentioned. So we run every model.

Okta (NASDAQ: OKTA)

Okta’s products help customers build more secure AI agents and manage their AI agents in a secure and scalable way; management thinks AI agents will redefine the identity security landscape; AI agents are also vulnerable without proper security governance, so it’s also essential for enterprises to secure AI agents; Okta has been focusing on securing AI agents (it is the company’s #1 priority now) and management thinks the space will be the next growth leg in identity security; management thinks Okta is the best-positioned to be the identity layer for AI agents; management recently launched Auth0 for AI agents, which allows customers to build secure agents; management has seen a recent surge in inbound interest for Okta’s solutions to manage the security of AI agents; it’s still early days for Okta in securing AI agents, but the company is already working with 100 current customers; management thinks Okta is the only company that is able to secure AI with a modern and neutral platform; the amount of interest in Okta’s solutions for securing AI agents is unlike anything management has seen; management is seeing a large number of enterprises getting stuck with AI projects because they’re unable to give the right level of access to AI agents; management thinks the market opportunity for the identity layer for AI agents is even bigger than Okta’s current opportunity set; only 10% of companies with AI agents in production think their agents are secured

The simple way to think about it is that Okta is helping customers both build more secure AI agents and manage their AI agents in a secure and scalable way. The emergence of agentic technology is redefining the identity security landscape. AI security is identity security. AI agents represent a new powerful identity type. However, without proper security governance, they are also highly vulnerable. Securing AI agents and nonhuman identities is not a feature. It’s essential for any businesses looking to safely scale their adoption and deployment of AI. If an organization does not secure its agents today, they risk undoing years of security improvements and leaving themselves vulnerable to new identity-based attacks.

Okta has prioritized our efforts to focus on helping customers solve this business imperative and capture what we believe will be the next catalyst for growth and meaningful market within the identity security space. Okta’s neutral and unified platform, coupled with our installed base of over 20,000 customers, positions us best to become the identity layer for AI agents. That’s why we’re so excited about the recent launch of Auth0 for AI agents. Auth0 for AI agents allows customers to build secure agents, APIs and users more effortlessly across their B2B, B2C and internal app ecosystem…

…Over just the past few months, we have experienced a surge in inbound interest for our Agentic Security solutions to manage agents, Okta for AI agents. These organizations are looking for a single control plane to observe and manage agents of all types in a way that offers flexibility as the technology continues to evolve. They also want a solution that gives them control like the ability to embed fine-grain access into every agent. Okta is here to deliver…

…It’s very early days on this front, but we have already been engaged with over 100 of our current customers, which combined represent over $200 million in existing ARR…

…Okta is the essential identity layer to help customers build, observe and manage AI agents. We’re the only company that is able to secure AI with a modern and neutral platform, allowing us to deliver even greater value to our customers…

…[Question] When you think about the full deployment of this, how do I think about the dollar potential here when you have customers that are spending $100,000 with you by how much can AI truly elevate that total bill for them?

[Answer] I’ve been personally and the entire company is blown away by how interested customers and prospects are in this capability. I haven’t seen anything like this in my experience at Okta with a new capability or a new product set. So it’s very, very exciting…

…You take all the company’s data and you show it in a big data warehouse like Snowflake or Databricks or Palantir and then the agents have way too much access. They can just see everything and they do unintended things. And so people are stuck and they’re pause and they’re saying, wait a minute, we’re not going to roll these things out. And there’s a huge, huge cohort of companies that are trying to do something AI and they’re stuck…

…Longer term, if you look at our market, we have a $50 billion TAM for workforce identity, a $30 billion TAM for customer identity. Owning and governing the agentic identity layer and securing AI can be a bigger TAM than both of those…

…The company’s #1 priority now is to take advantage of this opportunity. So we’re very clear in our R&D and our go-to-market, we’re going to focus on this opportunity…

…We shared a survey that we had run of a few hundred enterprise customers reporting that 91% of them had agents in production and only 10% of them were confident they had them secured.

A financial services company that is an existing Okta customer selected Okta for AI agents when it was deploying AI agents across its operations; the financial services company deals with sensitive data, so the security of its AI agents is critical; the addition of Okta for AI agents represented a significant ACV (annual contract value) uplift for Okta compared to the prior contract

A great early win with Okta for AI agents. It’s with a financial services customer that is in the midst of deploying AI agents across their operations. Given the sensitive nature of their data and the need to remain compliant with the regulatory environment, securing these agents was not optional. It was critical. They selected Okta for AI agents to secure their AI footprint and provide them with enhanced visibility and remediation capabilities for the agent identities, enforce access control, identity governance and threat detection. It was a great win-win. Okta is helping the customer to safely deploy AI across their business and the addition of Okta for AI agents represented a significant ACV uplift compared to their prior contract.

Okta’s management recently introduced a new open standard, Cross App Access, that helps with securing AI; Cross App Access enables AI agents to safely connect with other technologies; Cross App Access is now an extension of a model context protocol (MCP); customers of Auth0 for AI agents to build agents are getting Cross App Access out of the box

Last quarter, you heard me talk about Okta’s role in the development of cross-app access, which brings visibility and control to both agent-driven and app-to-app interactions. This allows IT teams to decide what apps are connecting and what information AI agents can access. I’m excited to share that as of last week, cross-app Access is now an extension of a model context protocol known as MCP, which helps validate that identity providers like Okta will act as the indispensable control plane for the AI enterprise…

…Customers that are using Auth0 for AI agents to build agents will get support for Cross App Access out of the box, meaning any agents that they build with Auth0 for AI agents will be discoverable by an IDP that also supports the model context protocol. And Okta’s IDP also supports cross-app access and the model context protocol. So customers developing agents with our technology will be producing agents that any company can secure more precisely. And the Okta platform will help customers discover agents that have been deployed and then manage those agents as well.

Okta’s management thinks that a key driver for customers to consolidate onto Okta is technological change; past technological changes have been cloud and mobile, but the recent change driving consolidation towards Okta has been AI; management has been working with a Fortune 50 customer on replacing a multitude of competing products with Okta, because (1) the consolidation will save costs for the Fortune 50 company, and (2) the Fortune 50 company is using 5,500 applications but only 1,500 are able to be hooked up to its central identity system with its existing solutions and this is not feasible for the Fortune 50 company’s agentic projects

[Question] From your perspective, what gets customers over the hump and convinces them to consolidate IAM, governance, PAM, customer identity and any other components to Okta?

[Answer] It’s always wrapped up in some other technological change. If you’re not changing your data center, if you’re not changing your apps, if you’re not investing in AI, you’re not going to change identity. So in all the customers I work with, it’s about some other catalyzing technological change. For many years, it was cloud and building mobile apps and still cloud transformation. And — but what we’re seeing more and more is companies are trying to move technology so they could take advantage of AI. They’re modernizing apps. They’re modernizing their security stack so they can give AI agents access to all of their data resources, and that’s been a catalyzer…

…We’re working with one of the largest Fortune 50 customer of ours on a wholesale replacement of Ping Identity, SailPoint, CyberArk, and several other identity vendors across their whole stack to standardize on Okta products. And the driver there is 2 things. It’s cost. They wanted to have less cost in their environment, and they want to have more better functioning and greater products. That’s part of the driver. But the bigger driver was actually something very simple, which is this company has 5,500 applications. And only all these years with these legacy vendors, they only had 1,500 of them hooked up to their central identity system. And so they’re thinking about agentic future where they want to give their agents and their agent infrastructure access to every application that they have, and they only had a paved path for 1,500 of them because they only were able to get that many on their identity platform with the old technology. So when they think about standardizing, they think about moving all 5,500 applications to Okta.

Okta’s management thinks agentic commerce will be a very big deal; management thinks Okta’s Auth0 for AI agent product is the right solution to secure agentic commerce

I think it’s a big deal. I think Agentic Commerce and if you have a website and you — that’s doing customer support or e-commerce commerce, you’re going to have some version of agents on there very quickly if you don’t already. And if you’re building those agents, Auth0 for AI agents is the right solution. It shortcuts the ability to have those agents connect to multiple systems on the back end. It helps you put Fine Grained Authorization inside of your agentic flow. So it’s purpose-built, and we’re — I think it’s a big trend we’re talking about here.

Companies have a few identity and security challenges when deploying AI agents, namely, (1) their agents need to be discovered, (2) ensuring agents are only authorised to do very specific things, and (3) knowing what agents have been deployed in their environments; Okta helps companies solve all the identity and security challenges that come with deploying AI agents

Builders of agents, they need to solve for at least 2 distinct challenges.

One is ensuring their agents can be discovered. And the second is ensuring that agents are only authorized to do specific things that they have access to specific corporate assets and not others. And Auth0 provides the capabilities to solve both of that with support for Cross App Access and model context protocol, agents built through Auth0 can be discovered and managed properly. And Auth0’s Fine Grained Authorization allows agents to be built in a way that their privileges can be very finely tuned, which is hugely important to our customers in that space.

But the second part of that challenge that our customers have is they don’t know. They tell us they don’t know what agents are deployed in their environment. They don’t know what their users have turned on and what their users’ agents don’t have access to. And this is the challenge of discoverability and being able to discover agents. So our — on the Okta platform side, our Identity Security Posture Management product scans corporate networks to find service accounts and the privileges of those service accounts, but it will also now help discover agents that are implemented and deployed as long as they support the Cross App Access protocol, the extension to MCP.

So the problem of discoverability is something they need help with, and we’re well positioned to help them with that. And the other related challenge is not only knowing that they exist, but then protecting the identity of those agents to ensure the agents can’t themselves be impersonated by a threat actor and to ensure that those agents are properly authorized to take the actions that they’re attempting to access.

So the Auth0 platform on the build side is hugely important for our customers and the Okta platform on the Discover and manage side is important for them as well. That also includes things like privileged access, allowing the agents to have tokens that are appropriately vaulted and governance, having them provisioned and be provisioned based on just-in-time requirements.

Okta’s management is seeing that most of the agentic projects companies are taking on involve agents that are built in-house; the deployment of agents by software vendors is a little slower

I would say that the actual most concrete implementations are agents they built themselves. I think that the deployment from the — some of the packaged application vendors you talked about are maybe a little bit more behind in terms of deployments.

Okta’s management is currently pricing Okta’s agentic products similarly to the company’s other products; the agentic products are priced on a per-agent basis; management is open to changing the pricing model based on what they learn, as the agentic model is still something new

The agentic products are priced similarly to our current products. Our current products are priced per user, the agentic products are priced per agent. So sometimes that can be a one-to-many relationship. You might have a few agents for a person. Sometimes they might be agents on their own. So I think we’re set up in a way that gives us flexibility as these things evolve in terms of how companies want to deploy agents to augment headcount, what they want to — how they want to deploy agents at the front end of processes before it ever gets to a person. And this is one of the advantages we have with all these customers and all this interest, we can figure this out quickly. And we can iterate on this quickly, and that’s how we’ve gotten to this pricing model because this is a new thing.

Okta’s management is currently not seeing major seat reductions at companies because of AI-related reductions in workforce; management is confident that Okta’s customer identity and agentic identity businesses will more than offset any reductions in its workforce identity business from AI-related reductions in workforce if it were to happen; management thinks a human employee will typically be bound to 5-10 AI agents

We are not — like everyone, we’re looking at what changes will happen in the global workforce at companies as they lean more on AI and technology to run their businesses. We’re not yet feeling a material headwind from — you mentioned seat reductions in the business. But were we to see that, we’re confident in our customer identity business offsetting that. We’re confident in our agentic identity business offsetting that. So in the aggregate, we view this shift in the industry as net upside for Okta…

…I think a lot of companies think about agents as like software engineering is a great example. As a software engineer, you’re going to have 10 of these agents working for you all the time. They’re going to be reviewing code. They’re going to be doing security reviews. They’re going to be checking code in. They’re going to be running tests. And that — all those agents are going to be working on your behalf in some cases and have their own identity and others and it’s just having the flexibility to support all those different use cases in addition to agents that would just run on their own. Your customer support agents or your agents sitting on your website accepting commerce are going to be on their own. They’re going to need access control, but they’re not bound to a user until maybe it gets lowered down in the workflow…

…[Question] What’s that relationship being like in the example that we’ve seen so far, what is it like 1 to 10, 1 to 20?

[Answer] I think it’s like 5 to 10 per person.

Okta’s Auth0 and Workforce agentic products are both experiencing similar traction from customers; the customer-profile of the Auth0 and Workforce agentic products are different

[Question] What’s getting more traction? Is it the Auth0 solution or the workforce side? And then what do you think represents the larger opportunity and why?

[Answer] They’re both getting about the same amount of traction. I think the — it’s a little bit different. I think a lot of the interest in the Auth0 for AI agents, it’s more online, people find out AI developers, right? So they find out about it on the website. They do self-service, upgrade to enterprise. It’s a little bit of a different motion. The Okta for AI agents, which is for IT and security, it’s very much have an enterprise architecture with a CISO or security influence buyer or an IT influence buyer.

Salesforce (NYSE: CRM)

Agentforce has delivered 3.2 trillion tokens to customers so far, exceeding management’s expectations; Agentforce and Data reached nearly $1.4 billion in ARR (annual recurring revenue) in 2025 Q3 (FY2026 Q3), up 114% year-on-year; Agentforce ARR reached $540 million in ARR in 2025 Q3 (FY2026 Q3), up 330% year-on-year; Agentforce is Salesforce’s fastest-growing product ever; management has integrated Agentforce into every Salesforce product; all of Salesforce’s data is unified for use in Agentforce; when an LLM is interacting with Agentforce, it’s getting strategic context from Salesforce’s data on customers, service, sales, marketing etc, and this data is unique because it makes business more valuable; 6 of Salesforce’s top 10 deals in 2025 Q3 (FY2026 Q3) are driven by companies who want to use Agentforce; Agentforce is only a year old, butSalesforce has already closed 18,500 Agentforce deals, 9,500 of which are paid; paid Agentforce deals are up 50% sequentially in 2025 Q3 (FY2026 Q3); Agentforce can rope in humans when necessary; Agentforce is using many different LLMs, including those from OpenAI, and will go with the lowest cost option; Agentforce can control AI costs by knowing when to invoke LLMs for tasks; Salesforce is itself an Agentforce customer; customers in production with Agentforce are up 70% sequentially in 2025 Q3 (FY2026 Q3); more than 50% of new Agentforce bookings in 2025 Q3 (FY2026 Q3) came from existing Agenforce customers; management launched Agentforce IT Service in November 2025; management thinks Agentforce is uniquely positioned for the agentic era partly because of its scale; new bookings for the most premium SKU management has within Agentforce doubled sequentially in 2025 Q3 (FY2026 Q3); customers leveraging Salesfroce’s forward-deployed engineers for Agentforce in 2025 Q3 (FY2026 Q3) saw 33% faster deployment times; 3 customers refilled their Agentforce tank in 2025 Q1, but 362 did so in 2025 Q3 (FY2026 Q3)

We have delivered incredible results with Agentforce. It’s really exceeding our expectations. You’re going to hear all the details, but I think that you could see 3.2 trillion tokens delivered for our customers…

…Agentforce and Data reached nearly $1.4 billion in ARR in the quarter, up 114% year-over-year, including Agentforce ARR of about $540 million, 330% year-over-year…

…This is our fastest-growing product ever…

…Every Salesforce app now not just sales, service, marketing, commerce, all of them, Tableau, Slack, our new ITSM, supply chain products, they’ve all been rebuilt, and Sreeni’s here, he’s going to talk about what we’ve done to bring Agentforce into every product we have and we transform Agentforce from being a product to a platform so that all of our apps can reason, learn, take action, collaborate with users, but it’s really about humans and apps and the AI and the data all working together. And that is what’s so exciting that every part of our platform is now so deeply integrated and because all of the data is unified. And every app shares the same metadata…

…When an LLM is interacting with Agentforce, it’s getting that strategic context from our data, from the data on the Internet as well, from the data that it’s been trained in. And then how you — knows how your business operates, it’s really able to give you that. And that’s because Salesforce is unique in that we have data that makes business more valuable. It’s that customer data, the service data, the sales data, the marketing data and then we’re able to deliver it in a tremendously friendly way…

…6 of our top 10 deals in the quarter are now driven by companies that just want to transform with Agentforce…

…A year since we introduced Agentforce, we’ve closed over 18,500 Agentforce deals. 9,500 of them are paid transactions, it’s up 50% quarter-over-quarter…

…Across the apps, you’ve seen the omnichannel supervisor like built into the service cloud, where all of a sudden, I’m a customer. I’m coming into the website even like Salesforce to help.salesforce.com or any of our customers’ websites. And I’m in there, and I’m working and then all of a sudden, I’ve hit kind of the limit of what the LLM can do, I can escalate immediately, also write to a human. And that’s where the humans and the agents and the AI and the data all have to work together…

…We use all of the large language models. The — they’re all great. We love all of them. We love all of our children, but they’re also all just commodities, and we can have the choice of choosing whatever one we want, whether it’s Open AI or Gemini or anthropic or what there’s other open source ones, they’re all very good at this point. So we can swap them in and out. The lowest cost the best one for us, making us basically the top user of these foundation models…

…As customers put Agentforce to work across their business, but not every task or step in the workflow needs to call the LLM, we call that determinism. And determinism is really important because for those of us who grew up in software, we used to call it if then statements, but now we call it determinism. But determinism is that, hey, if I need to do this, go to the LLM, but I probably don’t need to go to the LLM, just do that. So that is going to even reduce our costs further and not hit the LLM as much as we do. And that’s why we built hybrid reasoning and agent script and our AI teams are just crushing it on that. And we’re getting customers the best of both worlds, combining LLM driven reasoning and deterministic precision…

…We had strong performance across Agentforce Service, Agentforce Sales and Slack. And those 3 apps are just a powerful combination for [indiscernible] Salesforce. We use those every single day, we live on them. It is really the hat trick for Salesforce with large customers to say, “Let us show you what we’re doing in service. Let us show you what we’re doing in sales. Let us show what we’re doing in Slack. And it’s a Wow experience right now. It’s only going to get better…

…Customers in production with Agentforce have jumped now 70% year quarter-over-quarter…

…In the quarter, more than 50% of new Agentforce bookings as well as 50% of Data 360 in bookings came from existing customers, expanding their investment, which was awesome and really showed adoption…

…Last month, we launched Agentforce IT Service or Agentforce ITSM or you know that what company that we’re targeting…

…We’re delivering this capability to a global customer base, more than 150,000 Salesforce customers and 1 million companies are now on Slack, now have the immediate opportunity to work side-by-side with agents and Agentforce and the apps are already using every day to become elevated. And that’s why we’re uniquely positioned for this new area. We have the strategy of the platform, the global scale…

…New bookings for Agentforce One Edition and A for X or as we call it, Agentforce for Apps, our most premium SKU doubled quarter-over-quarter…

…Our top priority is accelerating Agentforce and Data 360 adoption. We are relentlessly reallocating our resources to high-growth areas and it’s paying off. Q3 was one of our biggest pipeline generation quarters ever and customers leveraging our forward-deployed engineers are seeing 33% faster deployment times…

…I don’t know if you remember 2 quarters ago, I was super excited. I had to dig very deep to find that 3 customers came and refill the tank in Q1. In Q3, 362 customers refill the tank. That’s an incredible testimony of the success that Agentforce is having in a very short time frame.

Salesforce’s management has delivered employee agents through Slack and it is called Slackbot; Slackbot is able to go through all of Salesforce’s customers’ data and do so in a secured way; Slackbot is able to deliver analysis of a customer and recommendations for interactions with the customer; management sees Slack as a conversational interface for every app, agent, and workflow; Slackbot is currently only available for a small number of customers; Slackbot is built on Agentforce

Some of you have seen it, but probably a lot of you haven’t as employee agents. And we’ve really delivered an incredible new framework deeply integrated into our Slack product. Every Salesforce employee already uses it every day I do, and it’s the core of every demonstration we give to our customers to show how we have unleashed with Slack, something new called Slackbot, which is really the heart of our employee agent strategy, and you’re going to see that. It’s incredible. It is able to go not only through Slack, but and not only through the whole Internet, but also through all of our customers’ data that they have basically provisioned in a secure way through Salesforce as well and deliver a context…

…I was with a really good friend of mine. Just this weekend, I had lunch with him, and he’s a top venture capitalist and he had been a huge investor in the Coinbase. And I’ll tell you that we’re just sitting there, just talking about, hey, tell me about everything with your venture capital company, tell me everything about this venture capitalist and then also tell me everything about Coinbase and the company and our relationship. And then it’s able to deliver to me an absolute and complete not only analysis, not only a summarization, not only all of the detail, but next steps, how to sell, what I should do exactly for the customer. And I love demoing this to customers because they don’t think it’s possible. And then when they see it, they say, “Wow, this is what AI was meant to be.”…

…And Slack is now where it’s coming all together, and that is this incredible conversational interface for every app, every agent, every workflow…

…They may not have Slackbot yet because we’ve only turned it on for a small number of customers who are about to hit the switch and everybody is going to see this employee agent power. So that most people have seen that customer agent power. Now they’re going to see the employee agent power. And they’re going to see how it’s built on Agentforce, how it’s built on the apps and how it’s built on the data.

Williams-Sonoma used Agentforce to build a digital sous chef on its website; there are no hallucinations with Williams-Sonoma’s agent; Williams Sonoma will be building voice agents soon; ride-hailing company Uber and consumer packaged food products company Conagra are customers of Agentforce; CVS Health, Telecom Argentina, TD Bank, the US IRS (Inland Revenue Service), and Costco have become Agentforce customers; General Motors is now an Agentforce customer and is using Agentforce to speed up case resolution for its call centers; PenFed has become a customer of Agentforce ITSM; Agentforce will help PenFed reduce operational expenses by 30%, and produce $2 million in savings; the UK police force recently launched Bobby, an Agentforce Service agent; Bobby is the UK public’s first point of contact for nonemergency calls and can provide instant responses; Bobby has already reduced nonemergency demand by 20%; Salesforce used Agentforce for STR Agent, which has generated tens of millions in incremental pipeline; Agentforce passed 2 million conversations in 2025 Q3 (FY2026 Q3) on Salesforce’s customer-help website; Agentforce took 9 months to reach the first million conversations on Salesforce’s customer-help website and 4.5 months for the next million

Williams-Sonoma’s version of Agentforce, which they call all of [ Olive ]. And if you haven’t been on the Williams Support — Williams-Sonoma’s website and seen the sous chef that they call Olive and used it, I think the quality is what I’m most impressed with that it’s really very, very good. You don’t see hallucinations. You see really kind of the customer personality, the quality, the ability to deliver value, and they are saying that’s about 60% of their chats. We’ve got a whole another level to go with them with voice, which is coming, which is very exciting…

…Great companies like Uber, like Conagra, like LY, like Williams Sonoma, like all these great companies that we’ve been talking about and the consumption flywheel is gaining traction…

…We had incredible wins this quarter, Miguel is going to talk about CVS Health and Telecom Argentina and TD Bank and the IRS, somebody who’s going to be getting a big check from all of us, they are all now on Agentforce. So your IRS agents or Agentforce agents and [ NG ] and so many more are becoming agentic enterprises. And Costco, we love Costco…

…We know General Motors, we love Mary, amazing, how one of her new Escalade IQ, she’s tired of me telling her how much I love it. Expanding Salesforce across the automotive cloud, Data 360, MuleSoft, Agentforce Sales, Agentforce service. But really cool Agentforce tossed their other collaborative product. We won’t talk — tell you what it is, you probably know the name. And they’re now using Slack…

…With Agentforce, Mary’s speeding up case resolution for her call centers. Slack is now the company’s primary communications hub, scaling to 96,000 employees in just 9 months…

…PenFed went live with ITSM with agents for IT service…

…You look at PenFed, I think they went live with agents for IT service as well as member service and collections, they’re projecting a 30% reduction in operational expenses and $2 million in savings with this product is killer…

…This week, we launched the U.K.’s first AI police officer. We work with multiple police departments to roll out Bobby. Everybody loves Bobby, it’s the Agentforce Service agent that is the public’s first point of contact for nonemergency calls and Bobby autonomously provides instant responses on more than 90 topics and police departments have already seen a 20% reduction in nonemergency demand, and they are just getting started, and this is what real enterprise adoption looks like…

…As Customer Zero, our STR agent, has worked hundreds of thousands of leads, generating tens of millions in incremental pipeline. We see that same velocity with Agentforce on help.salesforce.com, which passed 2 million conversations this quarter. It took 9 months to reach the first million and just half that time to double it, another clear example of our internal consumption flywheel taking off.

Salesforce’s management thinks that LLMs (large language models) are basically commodities

We use all of the large language models. The — they’re all great. We love all of them. We love all of our children, but they’re also all just commodities, and we can have the choice of choosing whatever one we want, whether it’s Open AI or Gemini or anthropic or what there’s other open source ones, they’re all very good at this point. So we can swap them in and out. The lowest cost the best one for us, making us basically the top user of these foundation models. 

90% of Forbes’ top 50 AI companies are using Salesforce, including the high profile AI companies such as Anthropic and OpenAI; the Forbes’ top 50 AI companies that use Salesforce average 4 clouds each; 80% of the Forbes’ top 50 AI companies that use Salesforce are using Slack

but nearly 90% now of all of the Forbes top 50 AI companies are using Salesforce. Let’s just think about that for a second. 90% of all the Forbes top 50 AI companies, those are the Anthropics and Open AIs and the [ blah, blah, blah ] companies, okay, that is our Cognition, Cursor, Figure AI, okay. They all average about 4 clouds each already. And 80% of them are using Slack to run their business.

Agentforce has powered 1.2 billion LLM (large language model) calls to-date, with 200 million calls in 2025 Q3 (FY2026 Q3); Agentforce is on track to power 2 billion LLM calls in 2026 (FY2027); Agentforce’s weekly actions have risen 140% quarter-on-quarter; Agentforce token usage in October 2025 was 540 billion, up 25% month-on-month

Agentforce has powered 1.2 billion large language model calls, that’s interactions when agents invoke a model to understand contacts and decide the next best action…

…More than 200 million Agentforce LLM calls in Q3 alone, on track to power another 2 billion over the next year. And those LMs now are calling these agent force actions such as updating the opportunities, creating a case, handling service inquiry and the number of average weekly actions has now risen about 140% Q-ver-Q…

…In October alone, token usage was nearly 540 billion, up 25% month-over-month.

Data 360 was formerly known as Data Cloud; Data 360 is the foundation for Agentforce; Data 360 ingested 32 trillion records in 2025 Q3 (FY2026 Q3), up 119% year-on-year; the 32 trillion records included 15 trillion zero-copy data integrations, up 341% year-on-year; traditional enterprises and technology companies alike are using Data 360; Data 360’s ingestion of records in 2025 Q3 (FY2026 Q3) was up 38% sequentially; Data 360’s zero-copy data integrations in 2025 Q3 (FY2026 Q3) was up 52% sequentially

Data 360 is the foundation for every Agentforce deployment, and it’s accelerating in Q3. Data 360, the product formerly known as Data Cloud. In Q3, Data 360 ingested 32 trillion records. 32 trillion records, up 119% year-over-year, and that includes 15 trillion through zero-copy data integration up 341% year-over-year. So Dentsu, Moody’s, KPMG, Ferguson, Zoom and dozens more invested in Data 360 in the quarter…

…In quarter-over-quarter on Data 360, people have built their lake, just in Data Cloud, our ingest has increased by 38%, and zero-copy has increased by 52% growth in terms of records.

Salesforce’s management sees the agentic enterprise as a new, very large, secular trend, after meeting many customers; management thinks that companies are finding it hard to build their own agentic solutions, so they need to vendors such as Salesforce; management thinks the agentic trend will lead to customers using Salesforce in a different way; management thinks the monetisation opportunity for Salesforce in the agentic enterprise trend is 3x-4x higher than before; Salesforce has already seen AOV (annual order value) with some customers increase by 2x-5x because of the agentic opportunity; the companies that are really turning to Salesforce’s agentic solutions are the visionary ones who started building their own agents 2 years ago, and they turn to Salesforce because they realised how bad the pain points were; the visionaries 2 years ago were concerned with what LLMs Salesforce was using, but now they no longer care

This past quarter, I was in 3 continents, 12 countries, I talk to 400 customers, many one-on-ones, many one to two several dinners. And the reality is very different. There is something very large, very important, and I want to emphasize this, I don’t think we’ve made Marc and Robin enough justice to what is happening right now in front of us. This is — there is a new very large secular demand trend, which is the agentic enterprise. Every single company in the world, small, medium, large wants to become an agentic enterprise…

…The problem is they’ve been experimenting. They’ve been experimenting for 2 years. They’ve gone from experimentation now to frustration a little bit. And now they are all saying, you know what, this is hard. This is much harder than we thought. They all want to go to scale because the opportunities, which is a multitrillion market cap opportunity, it’s in front of us. The TAM is a multitrillion for us, and they want to go all in, they know it’s hard because LLM cannot do this alone. And now to answer your question, the last mile is hard. And last mile is hard because companies need the context. For enterprise AI to be successful and accurate in the enterprise, you need the context, you need the data, you need the metadata, you need deterministic workflows. You don’t want the agents to be essentially executing based on what they found in an LLM, you want the agents to execute in a deterministic way the same workflows that that company had already qualified the apps for the years that humans are already using. And they need AI that is embedded where the humans are. That’s why it’s so important to have the data with the context to have the apps, the deterministic workflows to have the AI where the humans are and only Salesforce can do that…

…The Agentic enterprise is a new paradigm. Customers will have — we’ll use Salesforce in a totally different way. They will use Salesforce to be the platform for detailed labor for sales, for service, for marketing and the impact on the way we can monetize those relationships is exponential. It’s not linear growth. It’s exponential. Robin alluded to that at Investor Day, [ we were ] talking about 3x, 4 times the ability to multiply the monetization on customers because, by the way, they’re getting 3 or 4x or 10x more value from our products…

…The bookings that we do with them, the AOV had doubled, tripled, in some cases, multiplied by 4 and 5, and we are just getting started…

…When I talk to CIOs, I see 2 types. People who are really advanced who are visionaries who started 2 years back, do it yourself. they really understand the pain point. They are the ones who are moving fast to the platform…

…One more right thing is our customers 2 years back, they would ask me, what model are you supporting, where is it, what hyperscale you run. They don’t ask me any of those things now because we abstract all that complexity for them. That’s the original promise of Salesforce when we said no software.

Salesforce is not building data centers for AI, so its gross margin and cash flow is preserved

I just want to make sure everybody realizes we’re not building data centers at Salesforce. We’re preserving our gross margins and our cash flow.

Salesforce’s management thinks they have nailed down Agentforce’s pricing model by having a range of per-seat and per-consumption models; the per-seat Agentforce SKU doubled year-on-year in 2025 Q3 (FY2026 Q3)

The other thing that we’ve learned is pricing matters. It’s very complex. We’ve gone long ways. We’ve had different ways of pricing the product. And now I think we have the whole portfolio of different commercial frameworks to meet customers where they are where they want to be…

…You and me came up with the [ Agentic Enterprise License Agreement ] concept when we visited a few customers in Europe from Unilever to P&I We had great conversations. And we realized that they wanted to move. They wanted to transform, but they were afraid about all these metrics, consumption, et cetera. So we — what we’re doing now is very simple. We are putting the whole menu of options to them. We also have a very successful SKUs that we launched, which are Agentforce for sales or Agentforce for service that are seat-based SKU. People talk about seat versus consumption-based pricing. The reality is there are a lot of customers that want to seat based because seat-based gives you the predictability. So we’ve sold a lot of seat-based licenses for Agentforce and data cloud in Q3. In fact, that SKU has doubled year-on-year. It’s very massive success there. And — but we also have customers from the beginning that they want to just pay per conversation or per agentic actions. So we have the whole portfolio.

Salesforce is seeing both the number of seats and pricing increase

I think you guys always ask the same thing on whether the number of seats is increasing, the price is increasing. Well, for our clouds, we are seeing both increasing, which is exciting.

Veeva Systems (NYSE: VEEV)

The first AI agents under Veeva AI, an initiative launched in April 2025 that will see the company build industry-specific AI agents within its applications, is on track for a December 2025 launch; the first AI agents are for Vault CRM and commercial content; Veeva is on track for more agents in 2026, and these agents will be in all of Veeva’s software applications; early results of Veeva’s agents with early adopters have been very promising; management sees a lot of interest in Veeva AI from customers because they find value in specialised AI agents that fit seamlessly into their workflow; management thinks Veeva AI can be transformative for safety-related applications; management thinks AI agents will be transformative in clinical operations

The first Veeva AI agents will be available as planned in early December for CRM and commercial content. And we are on track for R&D, quality, and additional commercial agents in 2026. We started working with our first early adopters over the past few months, and early results are very promising…

…There’s a lot of interest in Veeva AI because of the clear business value in specialized AI agents working seamlessly in the user’s workflow. Customers are looking for practical solutions that address the specific needs of their functional areas and we are very excited about Veeva AI and what it can do for the industry…

…We are very pleased with our momentum in safety and the transformative potential of Veeva AI as applied to the safety area…

…We’re going to have agents in literally all of our software applications as we get through 2026. We started this year; we’ll have them in commercial and CRM and Commercial Content. Next year, in roughly the first quarter, April, it will be in Safety and Quality. And then through the end of the year, we’ll have agents in clinical operations and then by the end of the year, Clinical Data Management. We think it’s one of those potentially transformative areas in clinicals. It’s our largest single opportunity, the clinical business. There’s a lot of potential to just streamline a lot of core processes, eTMF, when you just intake a document and scanning through that and making sense of that with an agent as an example, just replacing core human labor with agents. So a lot of potential for productivity. That’s just one example, but I think we see that pretty consistently across the broader clinical area.

Veeva’s management thinks AI can change Vault CRM dramatically over the next few years, and customers are excited about it

Now we are entering the age of AI, probabilistic computing to really drive and change what a CRM system can do. So that’s giving people a lot of excitement. This — the Vault CRM of ’26 and ’27 and ’28, that’s not going to be like the Veeva CRM of 2022 and 2023. So that’s where the real excitement is.

Veeva’s management is seeing customers choosing AI partners based on where they think a particular partner can help them; management thinks Veeva can help customers to automate industry-specific applications with AI; customers want Veeva to go faster in AI, but the direction is very aligned; management thinks Veeva’s customers will require change management work to implement AI and this is where Veeva’s business consulting team can help; management thinks customers want an AI partner that can provide a one-stop-shop service for consulting, software, and AI

They want to use partners where partners can help them. So they want to use Microsoft where Microsoft can help them. They want to use Anthropic where Anthropic can help them. And they know where Veeva can help them is helping to automate industry-specific applications with AI, that deep domain knowledge and the business process consulting around it. So how do you enable insight generation in CRM through your field team by the use of compliant free text, okay? That’s a very specific thing. How do you dramatically increase the efficiency of Safety case processing for adverse events, okay? That’s very specific. So that’s what they’re looking to us for, and that’s what we deliver…

…They just want us to go faster, but there’s really rampant alignment on directions…

…Customers also have to be able to adopt and do that change management work, which is that’s not easy either. That’s not going to happen overnight. That’s one of our advantages is we have a great business consulting team…

…The customers are not going to want to knit together consulting over here and software over there and AI over here. They’re not going to want to do that over the long term.

AI’s impact on reduction in sales reps in the pharma industry has been lower than what management predicted; management thinks sales headcount in the pharma industry is going to be stable for a few years

[Question] I think there’s been some debate broadly on AI and how that may impact sales reps or like how efficient sales reps could be. Like as you talk to some of your customers, like how are they thinking about the size of their sales force with the implementation of AI?

[Answer] We have seen some of the reductions that have played out over the past couple of years that we have talked about. We kind of predicted roughly about 10%. It ended up being a little bit less than that. The way to think about it is the customers that they’re calling on the HCPs, number of doctors hasn’t fundamentally changed. You still need people. You need a base level of sales reps to build those relationships, cover those doctors, deliver the information, the service that they need. So I think the industry is cautious and thoughtful about making significant changes or adjustments. So I think there is a lot of potential for productivity gains and effectiveness gains. But I think it will likely be stable, at least for the next couple of years. We’re not hearing of any AI-related reductions.

Wix (NASDAQ: WIX)

Wix’s management thinks of vibe coding as having 2 spheres, one where developers live in, and the other where non-developers live in; vibe coding allows non-developers to create software; management sees parallels between Wix’s important role in website creation in the past, and nascent role in vibe coding in the present; management sees the vibe coding market as being much bigger than the website creation market; management has seen the vibe coding market grow exponentially over the past year, with Wix taking a bigger piece of the pie

When I think about vibe coding, I try to simplify things by breaking the world apart into 2 categories. One is the developer sphere. This is Claude Code, Cursor Windsurf and all these tools, which are great for engineers. These tools integrate directly on the source code of a project, enabling complex technical programming, which require significant user expertise. The second sphere is where everyone else lives, the majority of humanity who don’t code or even think they can code. Suddenly, with vibe coding, they can create pieces of software that improve their personal lives or help to build their businesses, all by simply using natural language. For example, a school teacher can create a custom app to track attendance and post grades. A neighborhood restaurant can build an application to handle their staff schedule, another to manage vendors, another to sort inventory and so on and so forth…

…This story sounds exactly like Wix’ story back in 2006. We didn’t invent websites back then. They were already widely available but only to big companies with engineering budgets. There was an absolute barrier for the average person. We knew there was a way to enable an online presence for everyone. This was and still is the mission of Wix. We intend to do for software, what we did for websites, enabling everybody to build applications without any need for a developer…

…The software application market is many, many times bigger than the website creation market. Think about it. That same neighborhood restaurant needs only one website, which they likely built on Wix, but they may need many applications to successfully run their business…

…The AI-powered app building space has grown exponentially over the past year, and we are taking a bigger and bigger piece of this pie.

Wix acquired Base44 in June 2025 (Base44 is an AI-powered platform that allows users to build web applications using natural language prompts); Base44’s share of audience traffic has increased from almost nothing in June to more than 10% in October; Base44’s capabilities are getting better fast, driven by a fundamental architectural advancement towards an agentic coding environment; Base44’s business has done better than expected since being acquired; the growth in Base44’s share of audience traffic was partly the result of the application of Wix’s proven strategic playbook; the returns on management’s initial marketing investments for Base44 meaningfully exceeded expectations; Base44’s userbase has increased 7x from June 2025 to 2 million today; Base44 has 1,000 new paying subscribers joining daily; management now expects Base44’s ARR (annual recurring revenue) to be at least $50 million by end-2025, higher than before; management expects Base44 to have similar operating and free cash flow margins as Wix in the long-term; management thinks many vibe coded projects are currently only prototypes, but they are already seeing some users build production-grade software with Base44 today; management thinks there’s still some way to go before vibe coding can be used to build production-grade websites

BASE44’s share of audience traffic increased from almost nothing to more than 10% in October. Among local tools, BASE44 is quickly proving to be a leader and the best solution on the market today with enormous white space, still ahead. BASE44 is also getting better, fast. We recently launched our new builder transitioning BASE44 from a predominantly user-reliant tool to an expert developer partner for everyone. The new builder represents a fundamental architectural advancement moving to an agentic coding environment. With multi-agent layers, BASE44 can now validate, debug, refactor for performance and fix its own work, making app creation faster, smarter and more powerful than before…

…We also welcomed our first full quarter of new BASE44 cohorts under the Wix banner in Q3, which performed better than anticipated. As the vibe coding market has exploded this year, BASE44 has meaningfully outgrown most peers. We now estimate our share of audience traffic to AI-powered application builders to be more than 10%, up from low single digits in June. This growth in a matter of just months is a result of a fantastic product with organic reach supercharged by our expertise and investments as well as application of Wix’ proven strategic playbook to BASE44.

In addition to establishing a dedicated customer care team and expanding BASE44’s R&D capabilities, we focused on building up a comprehensive full-scale brand and marketing function. Remember, BASE44 did not have any marketing motion when we acquired it in June. On day 1 after the deal closed, we started to apply a marketing plan that has been fine-tuned and tested over the past 2 decades, a key competitive differentiator for Wix to BASE44. This included refining the company identity, messaging and visual system to better reflect our market ambition. We also launched campaigns in key channels and core geographies, compelling branding and effective marketing is crucial to growing BASE44’s reach beyond just early adopters and capturing the huge white space Avishai spoke about. Returns on our initial marketing investments meaningfully exceeded expectations as demand ramped through the quarter. As a result, we were able to confidently scale marketing efforts above our initial August plan.

Today, BASE44 serves over 2 million users around the world. This is more than 7x more users than we had at the end of June. Impressively, this translates into more than 1,000 new paying subscribers joining daily. We now anticipate BASE44 to achieve at least $50 million of ARR by year-end, an increase from our previous expectations…

…In the long term, I expect BASE44 to have similar operating and free cash flow margins to Wix…

…You’re right, when you say a lot of it is just used for prototyping, right? And that’s great for people to actually build an application that is just for demo for a few people, and then the prototype is the application, right? It doesn’t need scale. It’s okay if it kind of like a tiny bugs. But we are getting to a place that today with BASE44, you can really build more full applications. There’s still quite a way to go on what we can do there and how to make it even better. But we are getting to a place. And of course, we have some users that already built really large applications that have been deployed and we can see that. So if a year ago, you couldn’t do by vibe coding for anything real. And a few months ago, you could do vibe coding for multi prototypes for applications, I think today we are starting to see more applications that are real and are being used in the commercial level.

For website, it’s still different. I think for website, there’s still a gap that needs to be closed, vibe coding to build real websites that are Google-friendly, that are LLM friendly, that are — the privacy rules that are required by law and a bunch of other things. And there’s still quite a distance to go, but we hope to close that early next year.

Wix’s management is already seeing AI costs decrease, and expects the trend to continue or even accelerate, as LLMs improve and competition ramps; management thinks there’s a lot Wix can do to lower AI costs, but it’s not a priority at the moment; the AI costs of new Base44 users is much higher compared to older users

Today, we’re already beginning to see AI cost decrease as LLMs improve and competition continues to ramp. I expect this to continue, if not accelerate…

…[Question] On the gross margins and the AI compute, is there anything that you can do within your control outside of LLM costs coming down to keep costs down, for example, using your own internal data to help build versus relying on third-party LLMs as much?

[Answer] I’m not going to go into all the details here, but yes, there’s a lot we can do on cost, okay? It’s not a priority at this stage, right? It’s something that we’re also investigating. I think the priority now is to build a better product and capture more market share. But I think that long term and log is not multiple years, we can dramatically improve the cost of AI for BASE44. There’s so much we can do from training our own models to do part of it from partnerships with the different vendors, from the fact the simple reality that cost is always declining. And so I think there’s going to be a tremendous amount of opportunities for us to reduce the cost of the AI for BASE44…

…New users coming to BASE, they are obviously consuming more AI tokens, right, more bandwidth as they build their apps. But what we see is a big difference between, obviously, the cost of newcomers. So the one that actually continue because they might modify, do some changes, but it’s really not the same.

Wix’s management thinks Base44 subscriptions will trend towards annual as users gain more trust; Base44’s churn rate is higher than core Wix at the moment, but management is optimistic this will improve with time; management sees Base44 monthly subscriptions performing similarly to core Wix monthly subscriptions; Base44 monthly subscribers are currently performing better than core Wix monthly subscribers in Wix’s early days

[Question] On BASE44. Can we just dive into the dynamics of monthly subs versus the sort of more traditional annual subs that you get for core Wix? What are you seeing there in terms of churn and those subscription dynamics? And as people sign up monthly, can you get them to sign up annually more often over time?

[Answer] At this stage, lean a lot more towards a monthly subscription than annual subscription. And we’ve also seen it in Wix in the beginning. It takes time for people to trust the platform, and then they will actually feel more comfortable to pay an annual subscription. And I think we are heading in that. Vibe coding is still so new that we’re heading towards that direction… When it comes to churn, it’s very early to say, and it’s changing very quickly. So it’s very hard to say. Obviously, churn is higher than the standard Wix, which almost doesn’t exist, right? There’s almost no churn. But if you look on a cohort basis. But Base is better than we expected and we know there’s so much more we can do. So we are very optimistic…

…[Question] Can you talk about the cohort retention trends of BASE44 and how it compares versus Wix on monthly customer plans?

[Answer] We’re seeing kind of similar behavior to what we know from the monthly on Wix. And I would actually dare to say that it’s better than what you used to see at Wix in the early days.

To prepare for an agentic future, management has made every Wix website indexable by LLMs, and has enabled agentic commerce functionalities; management thinks the user interface of websites will change in an agentic future

[Question] Wix is pretty well positioned to kind of reengineer the web for the AI era by making a lot of small business websites kind of agent ready, right? Like so they can be discovered by Gemini, ChatGPT and others more effectively versus the current web architecture, which includes a lot of total consumption for them. Can you talk about the vision you have for Wix for this era?

[Answer] The first thing that we’re doing in Wix in order to support and enable all our customers to enjoy that new mode is that every Wix website is now indexable by LLMs, right? So we make the data available to any LLM, and there’s a few formats for that. And so we ensure that ChatGPT can actually read your content and discover your website. That’s the first part. The second part is that we continuously add new standards for how to do e-commerce, the one that OpenAI released a few months ago, MCP, and a bunch of others in order to enable all the functionality to be available within LLMs or be discovered by LLMs and then run on your website. In addition to that, there’s a few more things that we think that how the user interface will change in the next couple of years. I’m not going to go into details, but I think that, that’s another super interesting opportunity for our customers.

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