Software development
fromInfoWorld
5 days agoThe reckless temptation of AI code generation
Replacing engineers with AI can lead to inefficient code and skyrocketing cloud costs.
Startup founders are being pushed to move faster than ever, using AI while facing tighter funding, rising infrastructure costs, and more pressure to show real traction early. Cloud credits, access to GPUs, and foundation models have made it easier to get started, but those early infrastructure choices can have unforeseen consequences once startups move beyond free credits and into real cloud bills.
A North American manufacturer spent most of 2024 and early 2025 doing what many innovative enterprises did: aggressively standardizing on the public cloud by using data lakes, analytics, CI/CD, and even a good chunk of ERP integration. The board liked the narrative because it sounded like simplification, and simplification sounded like savings. Then generative AI arrived, not as a lab toy but as a mandate. "Put copilots everywhere," leadership said. "Start with maintenance, then procurement, then the call center, then engineering change orders."
Have you ever thought about getting your small product into production, but are worried about the cost of the big cloud providers? Or maybe you think your current cloud service is over-architected and costing you too much? Well, in this episode, we interview Michael Kennedy, author of "Talk Python in Production," a new book that guides you through deploying web apps at scale with right-sized engineering.
"Cloud spending is growing fast - exponentially for some - and it's holding businesses back from investing in growth and innovation," said James Kretchmar, global CTO of the cloud technology division at Akamai Technologies.