Predictions by Priya Rajagopal, Director, Product Management, Couchbase

1. Success of edge AI will depend on advancements in lightweight AI models.

   • The innovation surrounding artificial intelligence (AI) is exciting, and edge computing is one way to enable new AI applications. However, in order to make edge AI a viable option, AI models need to be lightweight and capable of running in resource constrained embedded devices and edge servers while continuing to deliver results at acceptable levels of accuracy.
   • Models need to strike the right balance — meaning, models must be small and less computationally intensive so they can run efficiently at the edge while also delivering accurate results. While a lot of progress has been made in model compression, I predict that there will be continued innovation in this space, which when coupled with advancements in edge AI processors will make EdgeAI ubiquitous.

2. Only way to scale AI will be to distribute it, with the help of edge computing.

   • I predict that the convergence of edge and cloud AI is the way to deliver AI at scale with the cloud and edge offloading computational tasks to the other side as needed. For instance, the edge can handle model inferences while the cloud may handle model training or the edge may offload queries to the cloud depending on the length of a prompt and so on.
   • When it comes to a successful AI strategy, it’s not practical to have a cloud-only approach. Companies need to consider an edge computing strategy – in tandem with the cloud – to enable low-latency, real-time AI predictions in a cost effective way without compromising on data privacy and sovereignty.

3. AI tools will separate the good developers from the exceptional ones, playing an integral role in developer productivity.

   • I predict that AI tools will separate the good developers from the exceptional ones. Good developers will lean on AI tools to lighten their workload. Exceptional developers will use AI tools to boost productivity on repetitive, mundane tasks so they can focus more on being creative, tackling the hard problems and to handle the higher value tasks that promote innovation.

While I caution against developers getting too reliant on AI tools and leaning on productivity tools to do all or most of their work for them, the reality is that AI will continue to play a critical role in developer productivity, as long as developers understand the limitations of these tools and exercise good judgment when using AI tools. AI overuse can stifle innovation and critical thinking – and often the results from these tools may not be the most accurate, up-to-date or efficient way to solve the problem.