Decoding the AI Stack: Navigating Climate Technologies
The AI landscape can broadly be divided into four layers. In ascending order, these are the: Compute, Foundational Models, Infrastructure, and Application layer.
At the core of foundational models lies advanced computing power, essential for both creating and training extensive models. This demand is met by hardware innovators like NVIDIA, whose GPUs can handle large datasets in parallel, due to their architecture that supports thousands of simultaneous operations. We also have cloud-based platforms such as Azure that enhance these capabilities with HPC, which includes GPU clusters.
Climate applications often require multimodal, multidimensional data and in some use cases (e.g., Chemistry) to input real-world data, where we see robotic automation used for tasks like lab testing as computing resources.
The next layer is the foundational model layer, composed of large-scale AI models trained on vast datasets across a large range of internet text, images, or other data types. Extensive training allows these models to develop an extensive understanding of human language, concepts, and the world. Because they are trained on such diverse data, foundational models can generalize this knowledge to perform different tasks without being explicitly programmed for each one. One of the most known models is OpenAI, which has developed several foundational models, including GPT language models, Codex for computer code, DALLĀ·E for digital images, and more.
Horizontal foundational models often fall short for climate-related use cases due to their lack of specific data input and the deep learning instructions required for complex climate applications. Unlike LLMs, which learn the relationships between words, climate foundational models deal with multimodal, multidimensional data and need to comprehend the relationships between different dimensions. For example, energy foundational models might require training on intricate weather data. In the realm of chemistry, this could involve developing a standard language for chemical reactions, which necessitates an understanding of the highly complicated reactions initially.
The third layer is the infrastructure layer. It provides developers with a range of AI development tools, libraries, and frameworks. This layer offers the necessary resources and tools for developing and operating AI applications on a large scale.
When dealing with vertical infrastructure layers, such as those in the energy sector, partnerships like the Open AI Energy Initiative provide open frameworks. These are designed for energy operators and developers to build applications, encouraging the use of vertical foundational models in the sector. Additionally, we observe a trend where players initially build vertical foundational models but eventually enter the infrastructure and application layers, aiming to control the entire stack.
The fourth layer refers to the AI applications that utilize the underlying stack to accomplish specific tasks or address particular problems.
In the realm of climate applications, we see people building AI applications on both more mature horizontal foundational models and vertical-focused foundational models, depending on the use case's complexity.
Where can the greatest value be captured along the four layers?
Two use cases seem to be particularly interesting within the vertical climate layers:
Companies that already own the client relationship and have access to proprietary industry data that can effectively train on the relatively established horizontal infrastructure, package those models via applications, and ultimately deliver tremendous utility with fast time-to-value for customers.
Companies that build foundational models or infrastructure layers and can build a strong industry-specific data moat or the standard, open for other players to build infrastructure and/or applications on top of it.
This is a first outline for an ever-evolving framework, and there is still much to explore.
We are enthusiastic about continuing this discovery journey with you.
If you're building within any of the four layers mentioned above in a specific climate-related industry (like Energy, Mobility, Industry & Materials, Green Finance, Green Jobs, and Climate Risk), we encourage you to engage with us and challenge our discussed points. Likewise, if you're researching, educating, or exploring just as we are, we look forward to collaborating and exchanging thoughts, ideas, and frameworks.
PS: You can reach me at robina@cventures.vc