AI vendors move to tackle the hidden cost of inefficient enterprise code

"Enterprises don't often admit it out loud, but a good share of their cloud bills can be traced back to something deceptively mundane: inefficient code. A research report from software delivery platform provider Harness, which was co-authored with AWS, cited 52% of 700 engineering leaders and developers surveyed in the US and UK saying that the disconnect between finops and developers is leading to wasted spend on cloud infrastructure costs. "The reality today is that developers often view cost optimization as someone else's problem. This disconnect leads to over-provisioned resources, idle instances, and inefficient architectures that drain budgets," the researchers wrote in the report."

"Inefficient code is such a big part of that disconnect that it should be considered a CFO-level problem now, said HFS Research CEO Phil Fersht, because AI workloads are increasing power draw, carbon cost, and infrastructure spend. "Compute waste is enormous. Studies from large cloud providers indicate that 20 to 40% of cloud compute is underutilized or consumed by inefficient code. Enterprises pay for that waste," he said. This silent tax on compute has caught the attention of AI coding assistant providers."

"Google, for one, is zeroing in on it by unleashing a new coding agent, AlphaEvolve, that shifts focus from code generation to code evolution. The Gemini-powered coding agent is available in private preview, Google said in a blog post on Wednesday. Users must first write a definition of the problem they want to solve, a test to evaluate proposed solutions, and a first draft of the code to solve the problem. AlphaEvolve then iteratively applies Gemini LLMs to generate \"mutations\" in the code an"

Inefficient code significantly increases cloud infrastructure costs through over-provisioning, idle instances, and inefficient architectures. Fifty-two percent of 700 engineering leaders and developers surveyed in the US and UK said a disconnect between finops and developers leads to wasted cloud spend. AI workloads amplify compute waste, increasing power draw, carbon cost, and infrastructure expenditure; studies estimate 20–40% of cloud compute is underutilized or consumed by inefficient code. Vendors are responding by optimizing models and shifting from basic code generation toward iterative code evolution. Google introduced AlphaEvolve, a Gemini-powered agent in private preview that requires a problem definition, test, and initial code draft, then iteratively applies Gemini LLMs to produce code mutations.

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