“While the ClearBridge Multi Cap Growth Strategy has limited mega cap exposure, which has been a recent headwind to relative performance, we own several companies that stand to benefit from the explosive growth in generative AI. These holdings play key roles in building out the necessary infrastructure and helping customers leverage capabilities enabled by this emerging technology.

Semiconductor and software solutions provider Broadcom Inc. (NASDAQ:AVGO), for example, is an important supplier of networking chips that power ethernet switches and routers for connectivity between AI servers. The company sees quarterly revenue from this part of their business exceeding $1 billion in their fiscal third quarter, on a trajectory toward doubling over the course of the year.”

“Well, you can’t build any of these AI models without enormous amounts of training data. So if anything, what AI — generative AI has shown that the big issue about training one of these models is just getting that — this vast amount of data ingested into your GPU supercluster. It is a huge data problem in the sense you need so much data. To train — OpenAI to train ChatGPT 3.5, they read the entire public Internet, they read all of Wikipedia, they read everything, they ingested everything. And to specialize, and you take something like ChatGPT 4.0 and you want to specialize it, you need specialized training data from electronic health records to help doctors diagnose and treat cancer, let’s say, and we are partners.

Imaging for example, that is ingesting huge amounts of image data to train their AI models. We have our own — another partner of ours in AI, ingesting huge amounts of electronic health records to train their models. AI doesn’t work without getting access to and ingesting enormous amounts of data. So in terms of a shift away from data or a change in gravities at AI, AI is utterly dependent upon vast amounts of training data. Trillions of elements went into building ChatGPT 3.5, multiple times that for ChatGPT 4.0 because you have to deal with all the image data and ingest all of that to train image recognition. So we think this is very good for our database business, and Oracle’s new Vector database will contain highly-specialized training data like electronic health records, while keeping that data anonymized and private, yet still training the specialized models that can help doctors improve their diagnostic capability and their treatment prescriptions for cancer and heart disease and all sorts of other diseases."

“Oracle Corporation (NYSE:ORCL) (U.S.), a global software company, was the Fund’s top contributor for the fiscal year, with its share price rising primarily after reporting its fiscal fourth-quarter results. More recently, Oracle announced fiscal first-quarter results, which were in line with consensus expectations. The drivers of the core business are performing well, in our view, and management expressed confidence that annual revenue growth will accelerate as planned based on demand trajectory and its strong bookings trends. For the quarter, total revenue increased 8% in constant currency (9% reported), and operating income grew 12% with margins showing improvement. Cloud and support revenue grew 11% in constant currency, powered by Fusion +20% and Netsuite +21%. The “strategic back office cloud” is now up to $6.9 billion in run-rate revenue. Infrastructure cloud and support revenue grew 14% in constant currency, powered by infrastructure cloud services +72% ex-legacy hosting services to $5.6 billion in run-rate revenue. Momentum is continuing to build as Oracle signed several deals for its cloud business greater than $1 billion in total value during the quarter and booked an additional $1.5 billion in the first week of the second quarter. We continue to believe Oracle is an attractive holding and undervalued due to our perception of its intrinsic value.”

“We expect the product roadmap to continually evolve over time with additional GenAI announcements, Baird’s Rob Oliver and Patrick Schulz, who have a $540 price target on Adobe, said.

“Among our top contributors were Advanced Micro Devices, Inc. (NASDAQ:AMD), NU Holdings and Netflix. AMD’s data center CPUs are used in the cloud service provider (CSP) servers. In addition to the broader secular tailwind from cloud adoption, the company has a performance and pricing advantage over Intel, which we believe will enable it to continue capturing market share. However, the recent stock price rally was due to growing excitement around the company’s AI exposure. It will launch its new MI300 graphics processing unit (GPU) chip later this year to compete against the dominant market leader NVIDIA. Similar to its approach that won market share from Intel within the CPU market, AMD’s product will aim to provide similar performance at a more attractive price. AMD is already working with Microsoft and Meta, while Amazon publicly stated that it is evaluating AMD’s inferencing chips. Using assumptions around the total GPU market size, potential market share gains and price points, our research indicates this could be a $20 billion opportunity for AMD. That would nearly double its revenue. While the company has not historically missed many deadlines, there is execution risk as it works to manufacture and distribute these complex chips at scale, which, combined with an elevated valuation after the stock’s strong performance run, led us to trim the position.”

“The big news is Slack is really starting to wake up with its own AI. It holds so much data for our customers. I think Slack is going to be the promise of AI for a lot of our most important customers. If you go talk to any of these great, new AI companies they are all Slack-first companies; they all built their product with Slack.”

“Salesforce, Inc. (NYSE:CRM), a company we’ve owned since 2019, recently added ChatGPT-like capabilities onto its existing Al module, Einstein, to support its internal sales efforts and customer-facing software. For example, Einstein GPT can help generate marketing emails tailored to specific clients by using Salesforce’s customer database and past email correspondence to learn the most effective approach for each client. Einstein GPT is also different from off-the-shelf LLMS in three important ways: It keeps personal identifiable information private and secure, compared with external tools that retain anything a user enters. It employs the latest data in Salesforce’s system, as opposed to the sometimes-stale public data that train generic models. And generative Al capabilities can be integrated with other Salesforce offerings; the company has already introduced Slack GPT and Tableau GPT, Al-equipped versions of its workplace collaboration and analytics tools.”