More Of The Latest Thoughts From American Technology Companies On AI (2023 Q3)

Nearly a month ago, I published The Latest Thoughts From American Technology Companies On AI (2023 Q3). In it, I shared commentary in earnings conference calls for the third quarter of 2023, 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 2023’s third quarter after the article was published. The leaders of these companies also had insights on AI that I think would be useful to share. This is an ongoing series. For the older commentary:

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

Adobe’s management believes that generative AI is a generational opportunity to deliver new products and services

We believe that every massive technology shift offers generational opportunities to deliver new products and solutions to an ever-expanding set of customers. AI and generative AI is one such opportunity, and we have articulated how we intend to invest and differentiate across data, models and interfaces. 

The integration of Adobe’s generative AI Firefly models with the company’s Creative Cloud’s suite of products have led to more than 4.5 billion generations since their launch in March

The general availability of our generative AI Firefly models and their integrations across Creative Cloud drove tremendous customer excitement with over 4.5 billion generations since launch in March.

Adobe’s management has released three new Firefly models for different functions

The release of 3 new Firefly models, Firefly Image 2 model, Firefly Vector model and Firefly Design model, offering highly differentiated levels of control with effects, photo settings and generative match

Adobe’s Creative Cloud subscription plans now include generative credits; Adobe’s management introduced generative credits to Adobe’s paid plans to drive adoption of the plans and drive usage of the generative AI functions; management does not expect the generative credits (or packs) to have a large impact on Adobe’s financials in the short term beyond driving more customer sign-ups

We also introduced generative credits as part of our Creative Cloud subscription plans…

…Secondly, we priced the generative packs — sorry, we integrated the generative capabilities and credits directly into our paid plans with the express intent of driving adoption of the paid subscription plans and getting broad proliferation of the ability to use those…

… I don’t personally expect generative packs to have a large impact in the short term other than to drive more customers to our paid existing subscription plans.

Photoshop Generative Fill and Generative Expand are now generally available and are seeing record adoption, with them being among the most used features in the Photoshop product

The general availability of Photoshop Generative Fill and Generative Expand, which are seeing record adoption. They’re already among the most used features in the product.

Adobe’s management believes that Adobe Express’s generative AI capabilities are driving adoption of the product

 The family of generative capabilities across Express, including text to image, text effects, text to template and generative fill are driving adoption of Express and making it even faster and more fun for users of all skill levels.

Adobe’s management is seeing high level of excitement among customers for the Firefly integrations across Adobe’s product suite

Customer excitement around Firefly integrations across our applications has been great to see with community engagement, social interactions and creative marketing campaigns driving organic brand search volume, traffic and record demand. 

Adobe’s management expects generative AI features to deliver additional value and attract new customers to Adobe’s Document Cloud suite of products; generative AI capabilities for Document Cloud is now in private beta, with a public beta to come in the next few months and general availability (GA) to arrive later in 2024

Much like the Creative business, we expect generative AI to deliver additional value and attract new customers to Document Cloud. Acrobat’s generative AI capabilities, which will enable new creation, comprehension and collaboration functionality have already been rolled out in a private beta. We expect to release this in a public beta in the coming months…

…What we’re really excited about as we bring the AI assistant to market, which, by the way, as I mentioned, is now in private beta. Expect it to come out in the next few months as a public beta and then GA later in the year.

Adobe’s management is focusing Adobe’s generative AI efforts within its Experience Cloud suite of products in three areas: (1) Building an AI assistant, (2) reimagining Experience Cloud’s existing applications, and (3) creating new generative AI solutions

Generative AI accelerates our pace of innovation across the Experience Cloud portfolio, enabling us to build on our capabilities to deliver personalized digital experiences. Our efforts are focused in 3 areas: one, augmenting our applications with an AI assistant that significantly enhances productivity for current users and provides an intuitive conversational interface to enable more knowledge workers to use our products; two, reimagining existing Experience Cloud applications like we did with Adobe Experience Manager; and three, developing entirely new solutions built for the age of generative AI like Adobe GenStudio.

Adobe’s management recently released Adobe GenStudio, a solution with generative AI capabilities that combines Creative Cloud, Express, and Experience Cloud, to help brands create content; Adobe GenStudio is seeing tremendous customer interest

Release of Adobe GenStudio, an end-to-end solution that brings together best-in-class applications across Creative Cloud, Express and Experience Cloud with Firefly generative AI at the core to help brands meet the rising demand for content. GenStudio provides a comprehensive offering spanning content ideation, creation, production and activation. We are seeing tremendous interest in GenStudio from brands like Henkel, Pepsi and Verizon and agencies like Publicis, Omnicom and Havas as they look to accelerate and optimize their content supply chains.

Adobe now has a pilot program where some customers are able to bring their own assets and content to extend Adobe’s Firefly models in a custom way; Adobe is exposing Firefly through APIs to that customers can build Firefly into their workflows; Adobe is enabling users to integrate Firefly-generated-content into a holistic Adobe workflow

So with Firefly and Express, very excited about the momentum that we continue to see. You heard that we crossed 4.5 billion generations now so we continue to see really, really strong adoption and usage of it, partially as a stand-alone business but also integrated into our Photoshop and Illustrator and these existing workflows.

And we’re starting to see a lot of interest not just in the context of using it as part of the existing products but also using it as part of the ecosystem within enterprises. So we’ve been working with a number of customers to not just enable them with Firefly, which is the predominance of the growth that we’re seeing in Q4 for enterprise adoption but also have a number of pilot customers already engaged around custom model extensions so that they can bring their own assets and their own content into what Firefly generates.

Second, we’re also enabling the ability to expose it through APIs so they can build it into their existing workflows. And third, we’re, of course, connecting it and tying it all into Adobe Express, which now also has its own Firefly and additional capabilities like things so that you can not just sort of create content using Firefly but then start to assemble it, start to schedule social posts around it, start to do multi-language translations, that those are all features that are already in there and then create a stakeholder workflow from people working in Photoshop to the marketers that are trying to post externally. So that’s where things get very interesting and exciting in terms of the connection we have with GenStudio and everything that Anil is doing.

Adobe’s management intends to improve the generative capabilities over time, which might be more expensive in terms of the generative credits consumed, and management believes this will help drive Adobe’s growth over time

But what will happen over the course of the year and the next few years is that we will be integrating more and more generative capabilities into the existing product workflows. And that will drive — and we’ll be integrating capabilities like video generation, which will cost more than 1 generation, and that will drive a natural inflation in that market and that will become a driver for growth subsequently. 

Adobe’s management believes that Firefly is a great on-ramp for Adobe Express, and a great catalyst for all of Adobe’s products across the spectrum (the same underlying generative AI technology is also a great catalyst for Adobe’s Document Cloud business)

And that sort of brings them as an on-ramp into Express, which would be the other part. Express is certainly the introductory pricing, the ability to get millions more into the fold. And the ability right now, it used to be that Express and other offerings in that is to all worry about do I have the right templates? Well, AI is going to completely change that. We have our own models. And so Firefly will allow anybody to take whatever creative idea that they have and make that available. So I think Firefly really helps with the Express offering.

On the Creative Cloud, David mentioned this. I mean, if you look at the adoption of that functionality and usage that’s being driven, whether it’s in Photoshop right now, Illustrator, as we add video, both in terms of providing greater value, and we certainly will, therefore, have the uplift in pricing as well as the retentive ability for Firefly, that’s where I think you’re going to see a lot of the really interesting aspects of how Firefly will drive both adoption as well as monetization.

And then if you go at the other end of the spectrum to the enterprise, GenStudio, every single marketer that I know and CFO and CMO are all worried about how much am I spending on data? How do I get agility in my campaigns? And the fact that Firefly is integrated into both Express as well as when we do the custom models for them so they can upload their own models and then have the brand consistency that they want. So Firefly really is the fact that we have our own models, a great catalyst for business all across the spectrum…

… And then you take the same technology that we have in Creative and think about its impact in both Document Cloud when we do that and the ability to have summaries and have conversational interfaces with PDF, thereby making every single PDF, as David again said, both for communication, collaboration and creation far more compelling. I think you’re going to see that same kind of uplift in usage and therefore, monetization on the Acrobat side.

DocuSign’s management will be introducing generative AI enhancements to its CLM (Contract Lifecycle Management) platform; Veeco was an eSignature customer that has started using CLM, and DocuSign’s AI CLM features will help Veeco with surfacing actionable insights from customer contracts

CLM continues to grow well, particularly with North American enterprise customers. And for the fourth year in a row, our CLM solution was recognized as a leader by Gartner in contract life cycle management, noting our strong market understanding, product strategy and road map vision, including upcoming Generative AI enhancements. This quarter, we expanded a relationship that began more than 5 years ago with Veeco USA. Who’s the leader in workplace innovation. Veeco began using DocuSign eSignature and has added CLM as part of this transformation into a digital services company. Our AI solution will help Veeco streamline and enhance search and review of executed customer contracts with actionable insights to better serve its customers

MongoDB’s management held a customer feedback session recently and they saw four themes that emerged from the conversations, one of which was that customers of all sizes are interested in AI

This quarter, we held our most recent global Customer Advisory Board meeting where customers across various geographies and industries came together to share feedback and insight about the experience using MongoDB. From these discussions as well as our ongoing C-suite dialogue with our customers, a few themes emerge. First, AI is in nearly every conversation with customers of all sizes.

MongoDB’s management is seeing great early feedback from MongoDB’s partnership with AWS CodeWhisperer; MongoDB’s management also thinks that Microsoft Github Copilot is capable of generating useful code

We’re seeing great early feedback from our partnership with AWS’ CodeWhisperer, the AI-powered footing companion that is now trained on MongoDB data to generate codesuggestions based on MongoDB’s best practices from over 15 years of history. Microsoft GitHub Copilot is also proficient at generating code suggestions that reflect best practices in developers to build highly performant applications even faster on MongoDB.

MongoDB’s management is seeing software developers being asked to also build AI functionalities into their applications

And with the recent advances in Gen AI, building applications is no longer the sole domain of AI/ML experts. Increasingly, it’s software developers who are being asked to build powerful AI functionality directly into their applications. We are well positioned to help them do just that.

MongoDB’s Atlas Vector Search – the company’s AI vector search feature – recently received the highest NPS (net promoter score) among vector databases from developers; crucially, the NPS survey was done on the preview version of Vector Search and not even on the generally available version, which is better

In a recent state of AI survey reported by Retool, Atlas Vector Search received by far the highest Net Promoter Score from developers compared to all other vector databases available…

……As I said in the prepared remarks, there was a recent analysis done by a consultancy firm called [ Retool ] that really spoke to lots of customers, and we came out of top on — in terms of NPS. And by the way, our product was a preview product. It wasn’t even the GA product. 

MongoDB’s Atlas Vector Search allows developers to combine vector searches with another kind of search capabilities available in MongoDB, resulting in the ability to run very complex queries

Moreover, developers can combine vector search with any other query capabilities available in MongoDB, namely analytics, tech search, geospatial and time series. This provides powerful ways of defining additional filters on vector-based queries that other solutions just cannot provide. For example, you can run complex AI and rich queries such as “find pants and shoes in my size that look like the outfit in this image within a particular price range and have free shipping” or “find real estate listings with houses that look like this image that were built in the last 5 years and are in an area within 7 miles west of downtown Chicago with top-rated schools.”

MongoDB’s Atlas Vector Search allows customers to scale nodes independently, which gives customers the ability to achieve the right level of performance at the most efficient cost, so management thinks this is a very compelling value proposition for customers

One of the announcements we also made was that you can now do workload isolation. So for search or vector search functionality, you can scale those nodes independently of your overall cluster. So what that really does is allow customers to really configure their clusters to have the right level of performance at the most efficient cost. So we’ve been very sensitive on making sure that based on the different use cases, you can scale up and down different nodes based on your application needs. So by definition, that will be a very compelling value proposition for customers…

…[Question] With Vector Search comes quite a bit more data. So how are you making sure that customers don’t receive a surprise bill and end up unhappy?

[Answer] In terms of your question around the amount of data and the data builds, obviously, vectors can be memory-intensive. And the amount of vectors you generate will obviously drive the amount of usage on those nodes. That’s one of the reasons we also introduced dedicated search nodes so you can asymmetrically scale particular nodes of your application, especially your search nodes without having to increase the overall size of your cluster. So you’re not, to your point, soft for the big bill for underlying usage, for nonusage right? So you only scale the nodes that are really need that incremental compute and memory versus nodes that don’t, and that becomes a much more cost-effective way for people to do this. And obviously, that’s another differentiator for MongoDB.

MongoDB’s management believes that customers are aware that their legacy data infrastructure is holding them back from embracing AI (legacy data infrastructure do not allow customers to work with real-time data for AI purposes) but the difficulty in modernising the infrastructure is daunting for them; MongoDB’s management thinks that the modernisation of data infrastructure for AI is still a very early trend but it will be one of the company’s largest long-term opportunities

They are aware that their legacy platforms are holding them back from building modern applications designed for an AI future. However, customers also tell us that they lack the skills and the capacity to modernize. They all want to become modern, but daunted by the challenges as they are aware it’s a complex endeavor that involves technology, process and people. Consequently, customers are increasingly looking to MongoDB to help them modernize successfully…

… There is a lot of focus on data because with AI. Data in some way, it becomes a new code, you can train your models with your proprietary data that allows you to really drive much more value and build smarter applications. Now the key thing is that it’s operational data because with applications, this data is always constantly being updated. And for many customers, most of those applications are right now running on legacy platforms so that operational data is trapped in those legacy platforms. And you can’t really do a batch process of e-tailing all that data into some sort of warehouse and then still able to leverage the real-time use of that data. That’s why customers are now much more interested in potentially modernizing these legacy platforms than they ever have before…

…I would say it’s still very, very early days, we definitely believe that this will be one of the largest long-term opportunities for our business. we’re in the very early days.

MongoDB’s management has launched Query Converter, which uses AI to convert a customer’s existing SQL-related workflows to work with MongoDB’s NoSQL database platform, and customers have tried it out successfully

We launched Relational Migrator earlier this year to help customers successfully migrate data from their legacy relational databases to MongoDB. Now we’re looking beyond data migration to the full life cycle of application modernization. At our local London event, we unveiled the query converter, which uses genetic AI to analyze existing SQL queries and store procedures and convert them to work with MongoDB’s query API. Customers already tooled successfully to convert decades-old procedures to modernize their back-end with minimal need for manual changes.

MongoDB’s management thinks it’s too early to tell how the usage of MongoDB’s AI features by customers will impact MongoDB’s gross margin at maturity

[Question] And then the follow-up is more it’s around AI. So if I look at the demos that you guys have around vector search and how search is getting a lot better, that seems very compelling. And it seems like really straightforward for our clients to improve their the customer experience that they use it for a customer facing up, for example. What is the — what are the implications for gross margins for you, Michael, like do you have to do a lot more computer to be able to handle it?

[Answer] So I think it’s a little too early to tell. There’s obviously plenty of variability in the workloads depending on the nature what the underlying application is. So I think it’s a little early to give a strong direction to that… But I think too early to make a specific call or quantification on the gross margin impacts of AI.

MongoDB’s management thinks that Atlas Vector Search will be a big opportunity for MongoDB, but it’s early days and they find it hard to exactly quantify the revenue opportunity

We’ve seen a lot of demand from customers. And we feel like this is a big, big opportunity. Again, it’s early days. It’s going to take time to materialize, but this is, again, one of the other big growth opportunities for our business. That being said, in terms of the revenue opportunity, it’s really hard to quantify now because the use cases that customers are starting with are still kind of, I would say, early intent because people are still playing around with the technology. But we are seeing, as I mentioned, in UKG is using it to essentially provide an AI-powered assistant for its people. One Energy, European energy company is using terabytes of geospatial data and is using vectors to basically get better insights in terms of the images that they’re getting from the work they’re doing in terms of drilling for oil. So it’s still very, very early days. So hard to give you like an exact numbers.

When it comes to copilot tools for software coding, MongoDB’s management is seeing varying levels of productivity improvement for software developers based on the tools they are using; MongoDB’s management also sees the software written with copilots as being mostly for internal use currently

[Question] As customers began to trial some of these copilot code tools will say. What type of feedback have you gotten from them as it relates to the pace with which they’ve been able to reduce net new workload time to market, how much faster or efficient are customers getting using these tools?

[Answer] We get different answers from a lot of different customers. It really depends on which tool they’re using. Without commenting on who’s better, who’s worse, we definitely see a difference in the quality of the output between the different tools. I think it’s going to take some time for these tools to mature. So I think you’re seeing a lot of customers do a lot of testing and prototyping. I would also tell you that they’re doing a lot of this on internal-facing applications because there’s still lots of questions about IP rights and what is potentially copyrightable and then help to be licensable if they offer this as a shrink-wrap software or service to their end customers. So we’re seeing more of this work on internally facing applications but the productivity gains really do vary by tool and all the very do vary by the sophistication of the app being built. So it’s hard for me to give you a real number. I know there’s people out there quoting 30% or 40% improvement. But it really depends on the customer and the use case and tool that they’re trying to use.

MongoDB’s CEO, Dev Ittycheria, thinks his views – that (1) vector search would become just another functionality in a more holistic database platform, and (2) the database platform that can integrate vector search functionality well into developers’ workflow will win – has played out

I would say that I think 6, 9 months ago, there was a lot of interest in vector databases and there were some point solutions that got a lot of name recognition and a lot of people are wondering, is there a risk that we could be disrupted by them? And at that point in time, we made it clear that we believe vectors, we’re really another form of an index and that every database platform would ultimately incorporate vectors into their architecture. And the winner really would be the technology that made the vector functionality very integrated and cohesive as part of the developer workflow. I would argue that it’s really played out. 

MongoDB’s management saw customers having to work with two databases when performing vector searches for AI purposes; these customers were asking MongoDB to bring vector search capabilities into its database platform because working with one platform helps customers speed up their work and reduce costs

One of the reasons we actually built search is because we got feedback from our customers in many instances, a lot of our customers were dual homing data to MongoDB and to some sort of search database. So consequently, not only had to manage 2 databases, keep that data in sync, but also manage the plumbing that connected those 2 database platforms and customers told us they much would — this is like we don’t understand why you’re not offering a solution because we much rather have it all in one platform with one API. And that ultimately drove our desire to build out our search functionality, which is really becoming more and more popular. So the point for customers is that if you can remove friction in terms of how they can use the platform leverage the platform, have one set of kind of semantics in terms of — to address a broad set of use cases, it really simplifies the data architecture. And the more you simplify data architecture, the more nimble you can be and the more cost-effective you can be, and that’s what’s really resting with customers.

Okta’s management introduced Okta AI during the company’s Oktane event in October; Okta AI is powered by the data that Okta has collected over the years from its 18,800 customers and 7,000+ integrations, and is infused into several of Okta’s products

The headline of the event was the introduction of Okta AI, the identity solution for the next era of computing. Okta AI is AI for Identity. It’s powered by the massive amounts of data the company has accumulated over the years, including anonymized insights crowdsourced from our 18,800 customers and the 7,000+ integrations in the Okta Integration Network, as well as data on usage, policies, threats, and risk signals. Okta AI uses that data to perform powerful, real-time security, developer, and policy actions. Okta AI is also infused into several of our products. It makes our existing products more valuable and new products possible — all while expanding what it means to be integrated and protected.

An example of Okta AI at work is Identity Threat Protection, which enables companies to automatically log users out of apps during a security issue

Identity Threat Protection with Okta AI, a new product that will enable businesses to prevent and respond to threats faster than ever before. It empowers organizations to automate the detection and remediation of Identity threats across the tech ecosystem. It extends adaptive risk evaluation from the point of authentication to any time a user is logged in and helps you quickly prevent and respond to threats. Identity Threat Protection allows for an array of powerful new actions like Universal Logout. For the first time in our industry, it’s possible to automatically log users out of their apps during a security issue. Threat actors might be getting more sophisticated, but we are using the power of AI and our ecosystem to keep our customers safe and a step ahead.

Salesforce’s management thinks Data Cloud’s introduction was great timing because it coincided with the boom in generative AI and a company can’t make AI useful without data

And Data Cloud, this hyperscale, this real-time customer data platform that is performing incredibly well for us, it’s the foundation of every AI transaction, but it’s the foundation of every large deal that we did this quarter. That is what is so exciting. And in just our third quarter, Data Cloud has ingested an astonishing 6.4 trillion records, 6.4 trillion records. That’s 140% year-over-year increase. It triggered 1.4 trillion activations, a 220% increase year-over-year. This is a monster product. I could not be more excited. And it’s the perfect time, we didn’t really understand that it was going to line up so well with this generative AI revolution. It’s a product we’ve been working on for a couple of years. Just the timing of it has been incredible because listen, if you don’t have your data together, in a company, you’re not going to deliver AI. It’s not like companies are going to run their AI off of Reddit or off of some kind of big public data set. They have to have their data set together to make AI work for them, and that is why the Data Cloud is so powerful for them

Salesforce’s management believes that Salesforce is the No.1 AI CRM and is leading the industry in the current AI innovation cycle; they also believe that the current cycle is unlike anything they have ever seen and it’s a view that’s shared widely

We are the #1 AI CRM. If that isn’t clear already, we’re leading the industry through the unprecedented AI innovation cycle. It’s unlike anything I’ve seen and most of the people that I talk to all over the world feel the same way. 

Salesforce’s management believes that trust is going to be important in the AI era and Salesforce will be protecting customer data with a trust layer so that the data can’t be easily accessed by 3rd-party foundation models

Now as I’ve said before, this AI revolution is going to be a trust revolution. It’s not just about CRM, data or AI. It’s also about trust. And I think the trust layer and the way that we’ve architected our platform so that our customers are not basically taking — getting taken advantage of these next-generation large language models, these foundation models, they are so hungry for all of this data, and they want our customers’ data so that they can grow. We’re not going to let them have it. We’re going to separate ourselves from those models through a trust layer so customers can be protected. This is going to be so important for the future of how Salesforce architects itself with artificial intelligence.

Salesforce’s management is seeing customers across the world wanting to invest in AI for more productivity; management also travelled the world and noticed that customers are very excited about AI but at the same time, they are confused about AI’s capabilities – this excitement was not in place a year ago because generative AI apps had not surfaced yet

I’ve been on the road pretty much nonstop especially over the last month. I’ve been in — throughout Europe. I’ve been now in Asia. I’ve been throughout the United States. And I just continue to see these same trends, which is customers are investing for the future and they’re investing and inspired by AI to give them more productivity. Look, they realize unemployment is just so low. Where are they going to hire more people? It’s so hard for them to hire, they’re going to have to get more productivity from their employees. They’re going to do that through this great new technology, and we’re going to help them make that happen…

…And on a global basis, and like I said, in some of these customers in the last 30 days, I was in — I can give you my direct experience. I was in San Francisco, Los Angeles, Las Vegas, Stuttgart, Germany, I was in Nice, Monaco. I visited with our customers throughout that area. And also, I went up to Amsterdam, to France. I had a large customer dinner in the U.K. in London. I went to the U.K. Safety Summit. I then came back and went to Japan. I think I see something very consistently, which is customers are extremely excited about AI everywhere we go. It could be government, it could be commercial organizations. It could be technologists. Everyone is excited about AI. At the same time, there is a lot of confusion about what AI can and cannot do…

… And this excitement, this energy, these ideas of innovation of AI were not in place a year ago. Because don’t forget, a year ago, I don’t think any of us have used ChatGPT or Bard or Anthropic or Cohere or Adapt or any of the new AI companies. None of us had really had our hands on or envisioned what it really meant to us or that we would have Copilots, and that those Copilots would give us the ability to do all kinds of next-generation capabilities. But a year later, it’s a technology revolution. 

Salesforce has been deploying its own generative AI tools at a quick pace and management thinks the results have been excellent

I’ve been impressed with how quickly we deployed our own trusted generative AI tools and applications internally. We’ve launched Sales, GPT and Slack Sales, Elevate internally, and our global support team is live with Service GPT, and we’re seeing incredible results. We’ve streamlined our quoting process with automation, eliminating over 200,000 manual approvals so far this year. And since the introduction in September, our AI-driven chatbot has autonomously resolved thousands of employee-related queries without the need for human involvement.

Salesforce’s management thinks that every customer’s AI transformation is going to begin and end with data 

What I’ll tell you is you’re seeing something that we have been seeing and calling out for the last few quarters, but we probably have not been able to illuminate it to the level that you see now in the numbers, which is that every customer and every customer transformation and every customer AI transformation is going to begin and end with data. And for us to achieve that goal, those customers are going to have to get to another level of excellence with their data. 

Salesforce’s management thinks that there’s still a lot that AI-companies need to do to make AI safe for customers, but it’s getting better over time

We have — we still have a lot of work, as everyone does in our industry, on AI and making it safe for our customers. This is going to be incredibly important. I think for a lot of customers, they realize that they’d like to just let this AI unleashed autonomously but it still hallucinates a huge amount and it also is quite toxic. So we’re not quite ready for that revolution. But every day, it’s getting a little better. 

Salesforce’s management thinks that the movie Minority Report contains a good scene on how AI can be used to automate the personalised customer experience – management also thinks that this is something that many of Salesforce’s customers want to achieve for their own customer experience

And when I — going through the streets of Tokyo, it’s not quite the minority report, which is a movie that was partly written by our futurist, Peter Schwartz, but it’s getting closer to that idea. And when I walked into some of these stores, there’s definitely a lot more automation based on my customer record but not quite the level of automation that Tom Cruise felt when he walked into that Gap store, if you remember that scene, which was so amazing, which is very much front of mind for a lot of our customers because they want to have that capability and they want us to deliver that for them.

Salesforce’s management explained how Data Cloud can be very useful for companies that are deploying AI: Companies can use their own data, via Data Cloud, to augment generative AI models to produce personalised and commercially-useful output that otherwise could not be done

But they’re going to get frustrated when the Copilot that they are given from other companies don’t have any data. They just have data grounded to maybe the application that’s sitting in front of them, but it doesn’t have a normalized data framework on — integrated into the Copilot. So while I think Copilots on productivity applications are exciting because you can tap into these kind of broad consumer databases that we’ve been using. So as an example, the Copilot is I’m writing an e-mail. So now my — I’m saying to the copilot, hey, now can you rewrite this email for me or some — make this 50% shorter or put it into the words of William Shakespeare. That’s all possible and sometimes it’s a cool party trick.

It’s a whole different situation when we say, “I want to write an e-mail to this customer about their contract renewal. And I want to write this e-mail, really references the huge value that they receive from our product and their log-in rates. And I also want to emphasize how the success of all the agreements that we have signed with them have impacted them, and that we’re able to provide this rich data to the Copilot and through the prompt and the prompt engineering that is able to deliver tremendous value back to the customer.” And this date, this customer value will only be provided by companies who have the data. And we are just very fortunate to be a company with a lot of data. And we’re getting a lot more data than we’ve ever had. And a lot of that is coming from the Data Cloud because it’s amplifying the capabilities of all the other data we have. 

Salesforce’s management thinks that there will be significant improvements to Salesforce’s AI features in the near future

I think the demonstrations at Dreamforce were outstanding. The demonstrations that we’ll deliver in our February release will be mind-boggling for our customers of what they will be able to get done. And I think that by the time we get to Dreamforce ’25 or ’24 in September ’24, what we’ll see is nothing that we could have possibly imagined just 24 months earlier before these breakthroughs in generative AI have really taken hold through the whole industry.

Salesforce’s management thinks that no single company will control the development of AI because they think that open source AI models are now as strong as proprietary models and will lead the way; management also thinks that unlike the development of mobile operating systems which is controlled by 2 companies, there are thousands of companies that are working on open-source AI and this will lead to rapid innovation

No one company has a hold on this. I think it’s pretty clear at this point that because of the way AI is built through open source, that these models are very much commodity models, and these responses are very much commodity responses. So we’ve always felt that way about AI for more than a decade. We said that its growth has really been amplified by open source development. Because these open source models now are as strong as commercial models are or proprietary models, I think that what we really can see is that, that is going to accelerate this through every customer. There’s not going to be any kind of restrictions because of the proprietariness or the cost structures of these models. We’re going to see this go much faster than any other technology.

The reference point, as I’ve been using as I travel around, is really mobile operating systems. Mobile operating systems are very important, and we all have one on our desk or in our pocket right now. But really, the development of mobile operating systems has been quite constrained because they’re really held mostly by 2 companies and 2 sets of engineering teams. That’s not how this technology is being built. This technology is highly federated across thousands of companies and thousands of engineering teams who are sharing this technology. And because of that, you’re ending up with a rate of innovation unlike anything we’ve seen in the history of our industry and is moving us into areas very quickly that could become uncomfortable. So this is an exciting moment.

Veeva’s management has not seen a big impact on the clinical side of Veeva’s business from generative AI

In terms of the generative AI, honestly, I haven’t seen a big impact in clinical. There was good experimentation and projects around helping to write or evaluate protocols, for example, but not using things like generative AI to do statistical analysis or predict where the patients are. I think there, the more appropriate tool which people are using and continue to use more and more data science. Really having the right data, running the right algorithms, being systematic about it. So yes, I just haven’t seen that impact of generative AI. You see it more in other areas that relate to content creation and asking of questions, writing safety narratives, things like that.

Note: An earlier version of this article was published at The Good Investors, a personal blog run by our friends.

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Disclosure: Ser Jing has an interest in the shares of Adobe, DocuSign, MongoDB, Okta, Salesforce, and Veeva Systems.

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