Having more open, sustainable cloud infrastructures that lower barriers to entry, in terms of cost, accessibility and flexibility, can democratize GenAI

AI is becoming a de facto tool for everything from research and development through to production, post-sales services and achieving sustainability goals.

We are already seeing this with generative AI (GenAI): Large language models (LLMs) have clearly captured the imagination of organizations, and they are now accelerating interest in further GenAI capabilities internally and also through third party applications. 

However, what if an organization cannot connect all of its data or cannot scale its infrastructure? What impact does that have on its ability to take advantage of GenAI?

Selina Yuan, President, Alibaba Cloud Intelligence International

The Cloud-to-AI cost factor
Cloud computing, of course, becomes crucial here. How many businesses are actually being held back by budget constraints or the complexity of managing and updating disparate systems? Not all cloud infrastructure is equal. There needs to be a leveling of the playing field to give as many organizations as possible equitable access to empowering technologies.

Basically, for organizations to truly reap the benefits of GenAI, there is an urgent need for a more open, sustainable cloud that lowers barriers to entry, both in terms of cost but also in terms of flexibility and access. The world needs cloud pricing strategies that are designed to benefit long-term subscribers with significant discounts while also providing businesses with a stable foundation for developing their long-term strategies in planning and creating their AI applications.

Through affordable access to GenAI capabilities in the cloud, more organizations will be able to improve returns on investments in AI technology. 

Democratization for the GenAI boom
Over the next few years, cloud infrastructures need to be designed to be GenAI-centric, to drive innovation and action — with clear cost structures and scalability. Through an open cloud infrastructure, businesses can be empowered by greater transparency throughout the model-training process, as well as improved interpretability of AI algorithms. How?

  • There will hopefully be more open-source AI model communities where models and related tools and services are shared among global developers.
  • While there will be a co-existence of closed-source and open-source LLMs, the ability of open-source solutions to democratize AI should accelerate its adoption.
  • Open-sourced LLMs can drive growth of AI model communities, which prioritize collaboration for improved AI interpretability. In turn, this means that there will be more democratized access for organizations of all sizes and resources, to innovate on new or enhance existing products and services with the help of GenAI.
  • One example of open source collaboration that can widen the impact of GenAI is LLMs that provide enhanced support for local languages and diverse cultures of South-east Asia. The growth of these localized open-sourced LLMs will also drive the further growth of AI model communities that prioritize collaboration for improved AI interpretability.
  • The democratization of AI, and the delivery of open, GenAI-ready cloud services also means businesses will be able to devote more resources into organizational data, to make sure it is synthesized and usable by LLMs. After all, GenAI is great at summarizing and synthesizing data, but less impressive when it comes to gaining insights from unstructured data.

    • With democratized AI and cloud infrastructure, organizations can then address data issues without worrying about the underlying infrastructure. There should not be trade-offs. Cloud infrastructure has to become a de facto standard, forming a baseline for organizations to operate LLMs, to experiment, innovate and grow.