BMO Global Innovators Fund Active ETF Series-BGIN-Monthly Commentary
The Woodstock of AI
All eyes were on Nvidia’s GPU Technology Conference (GTC) as the world waited to see what cutting-edge, AI- enabled products and services would get announced. Excitement was so high this year, GTC was billed as the new “Woodstock” in terms of its anticipated significance and impact. It did not disappoint. We provide highlights and more in this month’s commentary.
What is GTC
Nvidia’s GPU Technology Conference (GTC) is an annual developer conference hosted by Nvidia. It serves as a key event for the company to make strategic announcements, introduce new products and technologies, and showcase innovations, particularly in the realms of artificial intelligence (AI), deep learning, and graphics processing units (GPUs). The conference gathers developers, researchers, and professionals from various industries to explore the latest advancements in GPU technology and its applications across different fields.
We have been following the announcements at GTC for years now as they have provided a prescient lens into the future of AI technology and the roadmap to come.
Why GTC Could Be the New Woodstock
Given the newfound status of Nvidia as a technology bellwether and AI thought leader, this year’s GTC has had an enormous amount of attention on it with a few calling it the “new Woodstock”. It may seem strange to compare Nvidia’s GTC (GPU Technology Conference) and the Woodstock music festival as they inhabit entirely different spheres — one rooted in the high-tech world of computing innovation and the other a historic cultural event symbolizing music and the counterculture movement of the 1960s. However, we thought it would be illuminating to expand on this to draw out some similarities between these seemingly disparate events.
Both events serve as a gathering point for communities with shared interests. GTC attracts developers, technologists, scientists, and various industry leaders passionate about artificial intelligence, graphics processing, and technological innovation; Woodstock brought together hundreds of thousands of people unified by music, peace, and a desire for social change.
Woodstock was about pushing cultural boundaries, exploring new forms of artistic expression, and embracing innovation in music and social ideas. Nvidia’s GTC, though focused on technology, embodies a similar spirit of innovation by showcasing breakthroughs in AI, graphics technology, and computational capabilities.
Both events will leave a lasting impact on their participants and broader society. Woodstock is remembered as a pivotal moment in music history and a symbol of the 1960s counterculture. Nvidia’s GTC, especially with announcements like the introduction of the world’s most powerful chip for AI, seeks to transform industries, shape the future of AI research and application, and impact how technology will evolve to address complex challenges.
Both are an incredible showcase for talent. Just as Woodstock was a platform for emerging musicians to gain wider recognition, GTC serves a similar purpose for scientists, researchers, and companies – to showcase their latest work, share discoveries, and earn recognition within the tech community. These researchers are the future rockstars of AI.
Just as Woodstock inspired a generation of music lovers, GTC is inspiring the current and next generation of technologists and developers to innovate and push the boundaries of what is possible with AI and computing.
Our last point is that Nvidia’s CEO and co-founder Jensen Huang’s extensive wardrobe of stylish leather jackets look equally at home on either the AI conference circuit or on a rock and roll stage1 (see next page).
GTC Highlights
Jensen Huang, CEO of Nvidia, made several announcements during the event, indicating the company’s ambitious plans in the AI sector. Nvidia unveiled the Blackwell B200 GPU at the GTC 2024, claiming it to be the “world’s most powerful chip” for AI, designed to enhance the accessibility and efficiency of AI models with trillions of parameters. This chip, boasting 20 petaflops of computing power and 208 billion transistors, in combination with the GB200 (which integrates two B200 GPUs and a Grace CPU2), aims to dramatically increase LLM inference3 workload performance by 30 times and reduce costs and energy consumption by up to 25 times compared to the H100 model. Moreover, Nvidia announced collaborations with major tech companies like Microsoft, Google, and Amazon to integrate its AI tools into their cloud services, although notable AI providers like OpenAI and Anthropic were absent from the partnership list.
Nvidia’s strategy to transition from selling individual chips to offering complete systems, reflects its comprehensive approach to AI technology deployment. Nvidia’s expansion into software for 3-D model simulation and immersive technology, alongside its ventures in many industries, underpin its continued leadership and innovation in tech and AI realms. Drug discovery using AI was a notable theme that has been gaining traction ever since AI assisted with the prediction of 200 million 3D protein shapes.
Other announcements included significant partnerships in the automotive industry with leading Chinese electric vehicle makers such as BYD and Xpeng. Furthermore, Nvidia introduced new software tools to facilitate businesses in deploying AI models more easily, aiming to expand its influence in the AI inference market.
AI-Based Drug Discovery
There were a series of advancements in the field of AI applied to drug discovery, genomics, and life sciences. Nvidia has created BioNeMo, a generative AI platform for drug discovery, foundation models like DNABERT for analyzing DNA sequences and their mutations, scBERT, aimed at single-cell RNA sequencing data analysis, and EquiDock, that predicts the 3D structure of protein interactions facilitating drug discovery.
Nvidia also launched NVIDIA Inference Microservices (NIMs), optimized cloud-native microservices aimed at simplifying AI model deployment across various platforms. This is intended to make AI models more accessible. NIMs support a range of applications in healthcare, including drug discovery models like DiffDock, ESMFold, AlphaFold2, MolMIM, and Universal DeepVariant, which significantly accelerates genomic analysis.
Additionally, Nvidia announced collaborations with companies such as Cadence Design Systems for accelerating drug discovery, and partnerships with AWS and Microsoft to enhance life sciences research and healthcare applications through advanced AI and computing solutions.
Over 100 companies, including Astellas Pharma, Iambic, Insilico Medicine, Recursion, Johnson & Johnson, GE Healthcare, and Terray Therapeutics, are utilizing Nvidia’s BioNeMo for various drug discovery applications and Nvidia’s Clara for medical device workflows, showcasing the platform’s widespread adoption in the pharmaceutical, biotechnology, and medical technology sectors. Insilico recently announced that its AI-created drug has just been approved for Phase 2 trials4.
Not the Only Music Festival in Town
GTC wasn’t the only festival in town focused on AI innovation - Adobe also hosted an event. The main takeaways from the Adobe Summit include the announcement of new products related to Firefly and an improved communication by management on new AI revenue streams. Although no formal change in guidance was presented, the company did have a net positive outlook with notable enthusiasm for the second half of the year and beyond. Key positives included Firefly product innovation such as custom models and new services across the Creative Cloud suite and future expansions into 3D, video, and audio models by year’s end. While investors are enthusiastic about their outlook, concerns linger about whether AI will disrupt overall Creative Cloud seat count growth and other questions about new competition from Canva and others that are aggressively integrating and applying generative AI into their products. Despite concerns, we still believe that Adobe has a good position in this market given the expectation that demand for editing tools will increase. This is clear to anyone who has used AI to generate an amazing image only to discover they need to clean up a notable defect that detracts from the otherwise amazing output.
Conclusions
There continues to be an incredible level of research and development in AI that is leading to incredible discoveries. We are in early innings of the applications and continue to believe we will see more commercial applications this year from of our portfolio companies. Conferences are a great way to see what is in the innovation pipeline and we eagerly await this year’s Collision conference hosted in Toronto.
1 https://www.nytimes.com/2023/06/14/style/jensen-huang-nvidia-leather-jackets.html
2 CPU: central processing unit - the primary component of a computer that acts as its “control center.”
3 LLM inference: LLMs are large language models; LLM inference is the process of entering a prompt and generating a response from an LLM. It involves a language model drawing conclusions or making predictions to generate an appropriate output based on the patterns and relationships to which it was exposed during training.
4 https://www.eurekalert.org/news-releases/1037177
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Publication Date: April 2024