Leveling the Playing Field for AI Beneficiaries

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It’s impossible to overstate the impact of generative AI on the overall market. The technology is projected to generate between $2.6 trillion and $4.4 trillion annually, increasing the economic impact of all AI by 15% to 40%. That figure could even double if generative AI were embedded into software used to solve other problems. So how do we operationalize it?

For all the talk about AI taking jobs, economic analysis and history suggest that productivity gains are far more likely than job destruction. But which ones?

Let's start with developers. Some case studies have shown a 25-50% increase in developer productivity , which is a lot. But what will they do with that freed-up time? They probably won't be able to carve out time to work on the technical debt they know they've accumulated. Instead, the business will demand more features, and that can impact other teams. It's like a balloon. When you squeeze it too hard on one end, you need to think about what's going to happen on the other end. You don't want it to pop.

The key is to consider the impact of increased productivity on denmark mobile database teams. What happens to operations and infrastructure teams? What happens to platform teams, system reliability engineers (SREs), and network operations center (NOC) staff? If developers ship more code to production and accumulate technical debt faster, it can overwhelm the teams that maintain that code in production.

Part of the solution is process (which we’ll talk about next), and part of the solution is addressing the disparity in how generative AI benefits. So the question becomes how to ensure that non-developer teams have a chance to share in that 25-50% productivity gain. Generative AI can certainly be part of the answer by helping automate operational tasks like standardizing scripts, making them easier to translate from bash scripts to Python, etc. Another example: platform and SRE teams can improve productivity by automating the process of creating simple runbooks.

DataOps: Supporting Modern Data Architectures
Next comes the process. It’s easy for engineering teams to get caught up in their own features and not pay attention to the broader experience. There’s a lot to consider to get LLM into production, from hint engineering to pricing. But to effectively ship high-quality products, organizations need to see the big picture: the entire product from start to finish. This means that when implementing generative AI or any other AI capability, LLM inference is just one part of the overall experience.