3 Takeaways from the World Economic Forum’s Global Technology Retreat

Global leaders across tech, government, industry, start-ups, and NGOs met in San Francisco to discuss and ideate on one main topic: How do we take advantage of the incredible opportunities of AI, quantum computing, and synthetic biology to drive positive economic growth and mitigate climate change?

1. AI is everything, everywhere, all at once

AI is developing at a breakneck pace, with new models being launched almost weekly. While we tend to think of generative AI as a new area, it was highlighted multiple times that the area has been underway for a long time and that the field started accelerating in earnest around ten years ago, building on decades of research involving hundreds of researchers across continents.

Generative AI is quickly evolving with the emergence of multimodal, multi-language models, and general-purpose ones being tailored for specific verticals and needs. This creates opportunities for new use cases but also obvious challenges as it makes it more difficult to ensure models behave as expected.

The need for a multistakeholder approach is evident, requiring collaboration among companies, venture capitalists, industry groups, and standard-setting bodies to ensure continued development and responsible deployment.

2. Industrial companies face unique challenges deploying AI

Industrial companies are focused on driving efforts across sustainability, automation, safety, and electrification not just because they drive business value but because they are seen as a license to operate. Deploying AI, in short, must both demonstrate tangible and rapid returns on investment, not just for the company but also for the surrounding communities.

Reflecting on the use cases of initiatives like the WEF Factory of the Future from five years ago, it becomes evident just how rapidly technologies and use cases evolve. Use cases such as energy management and robotics automation now appear commonplace. Similarly, today's AI applications, like document parsing and copilots, may soon become routine.

3. AI growth drives incredible demand for power, contributing to climate change

Climate change and climate resilience are still at the top of everyone’s mind, even in this AI craze. In part because of AI, as its demand for power to train models and run them in the cloud puts pressure on the grid. Not only that, but the massive build-out in data centers being undertaken drives demand for everything from land to steel to switchgear components.

Thankfully, data and AI are also the enablers that can make this build-out of AI computing capacity more efficient. There are rapid increases in the efficiency of chips, the efficiency of data center operations as well as at the software level via the optimization of model training. This has resulted in 3x the computing power over the last 5 years using the same amount of power.

Cognite’s Perspective

What's striking is the swift transition of cutting-edge research from the lab to practical applications worldwide. This immediate adoption comes from the tangible business value that AI is offering end-users across companies to do their job more effectively. Overnight, AI has become an integral part of the way many companies operate, shaping new ways of operating especially in areas that benefit from clean and structured data such as retail and financial services..

Industrial companies must grapple with the practical constraints of their legacy plants and operations though, plants that have often been in operation for decades and with complex and messy industrial data being trapped in silos. The need for contextualization of this messy, industrial data was brought up multiple times during the Technology Retreat, especially because it is the digital transformation of energy and industrial companies that is needed to drive lower emissions in the hard to abate sectors and ultimately mitigate climate change.

Contextualized data is a necessity to reliably deploy AI in industrial operations. At the same time, true digital transformation is hard, and AI efforts demand substantial investment, which means that the past focus on Proof of Concepts for digital use cases must be refocused on achieving business value at scale.

Industrial companies need an experienced partner like Cognite who has deep domain expertise and understands the unique operating challenges of the day-to-day operations of industrial companies. If you are looking to explore how AI can drive impact at scale for your business, please reach out for a deeper conversation.