At Asia Risk Congress yesterday, I shared my prediction that open-source AI will redefine the future of financial institutions in Asia. Going forward, institutions will shift towards open-source models they can fine-tune, manage, and scale internallyโgiving them a competitive edge in a rapidly evolving landscape.
At its core, AI is built on three key pillars: Data, Algorithms, and Compute. Here’s why I see open-source AI become a driving force, propelled by Metaโs singular strengths:
1๏ธโฃ ๐๐ฎ๐๐ฎ: Meta has unparalleled access to massive volumes of (your ๐) text, images, and video data, vital for training the next generation of multimodal AI models.
2๏ธโฃ ๐๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ๐: Metaโs strong engineering culture continuously pushes the boundaries of AI capabilities.
3๏ธโฃ ๐๐ผ๐บ๐ฝ๐๐๐ฒ: With immense financial resources from its core business, Meta has the infrastructure to support AI at scale.
๐ช๐ต๐ฎ๐ ๐ฑ๐ผ๐ฒ๐ ๐๐ต๐ถ๐ ๐บ๐ฒ๐ฎ๐ป ๐ณ๐ผ๐ฟ ๐ณ๐ถ๐ป๐ฎ๐ป๐ฐ๐ถ๐ฎ๐น ๐ถ๐ป๐๐๐ถ๐๐๐๐ถ๐ผ๐ป๐?
Just like with cloud computing a decade ago, financial institutions initially embraced it, but quickly realized that adoption was complex and costlyโespecially in a regulated environment. As a result, on-premises and hybrid infrastructures still dominate today.
I see a similar path for AI: the future will be ๐ผ๐ฝ๐ฒ๐ป-๐๐ผ๐๐ฟ๐ฐ๐ฒ ๐น๐ผ๐ฐ๐ฎ๐น ๐๐โAI that institutions can control and manage themselves.
Metaโs release of Llama 3.2 just two days ago coincidentally perfectly illustrates this. Llama isnโt just another modelโitโs a comprehensive AI ecosystem that allows institutions to:
โก๏ธ Download models directly from Metaโs website.
โก๏ธ Fine-tune models with torchtune.
โก๏ธ Deploy AI systems using frameworks like ExecuTorch, TorchChat, and OLLaMA.
Ecosystems like LlamaStack are paving the way for financial institutions to start customizing and managing AI models in-house, driving deeper AI adoption.
Much like ImageNet accelerated computer vision and PyTorch democratized deep learning, LlamaStack will make open-source AI more accessible and customizable, further strengthening Meta’s moat.
Big tech will dominate in algorithms and compute power. So, if financial institutions want to compete, they need to focus on Data.
In Asia’s context of diverse languages and cultures, the real winners will beย those that have strong data foundations, upskill their teams to work with open-source models, and develop the capacity to leverage local, adaptable AI at scale.
