AI enablers are the foundational software infrastructure tools and components that support, scale and streamline AI-native applications and workflows.
According to venture capitalist Sumedh Nadendla, they are also the big trend to watch for in tech.
Said the investor in a recent article, comparing the boom in AI today to the earlier internet era, “In the early era of the internet, the dominant telcos of the time had to invest heavily in building the physical backbone required for widespread internet adoption, much like the hyperscalers of today. Without these investments, the internet would have struggled to get off the ground.”
“The promises of the internet and creation of new markets led to rapid technological innovation, albeit without established standards or protocols in place. This led to the rise of software infrastructure companies providing technologies such as database systems, networking infrastructure, security solutions and enterprise-grade storage.”
Clearly seeing a parallel between both eras, he added:
“We can see a highly similar pattern shaping up today when we examine the progress of AI adoption. Although we have the nuts and bolts of functional AI tools — often referred to as ‘point solutions’ in venture circles — achieving widespread and meaningful adoption of AI will largely hinge on emergence of AI enablers.”
The role of AI enablers and three themes were also covered in depth during the article. These include:
1. Durable cloud workflows: Enable reliable, reproducible results
As AI moves from the lab into the hands of enterprise-scale users and major public institutions, “good enough” won’t do. As with any service, AI needs to be reliable, offer reproducible results or idempotency, and minimize the risk of faults that undermine the viability of the technology during this pivotal moment of adoption.
2. Resource management tools key to efficient deployment
The resource management tools we call AI enablers make it easier to use databases, streaming, storage and caching. Disparate tools cobbled together under poorly structured frameworks can not only drain both computing and financial resources, but also lead to a misallocation of engineering effort and focus. Current cloud deployments are optimized for non-AI applications, and as we transition to an AI-native world, these resource management tools would be helpful in building more efficient and capable workflows.
3. Leverage DevOps for agile AI solutions
Optimized DevOps tooling is essential for accelerating development cycles, unlocking developer productivity and enhancing software quality. While we can see the incredible potential of rapid prototyping tools such as CoPilot, Cursor and Loveable, new and novel tools for automated testing and remote build execution are making the task of creating quality code much simpler. Taken together, these tools provide developers with 10x capability, turning ideas into production-ready systems faster than ever.
To read his article in full, visit here.