AI tools have reshuffled where effort goes in a web project — but they haven't made professional websites cheap. This episode breaks down what's actually changed about design costs in 2026, and what still requires human judgment and budget.
Budgeting for a new website in 2026 means navigating a landscape that AI has genuinely disrupted — but not in the way most people assume. This episode of DEV.co examines the real breakdown of website design costs in 2026, separating the hype from the concrete shifts that affect what you should actually plan to spend. Spoiler: the total budget doesn't disappear. It redistributes.
Here's what the episode covers:
The core takeaway: AI has shifted effort within a web project, not eliminated it. Hours that once went into grinding through wireframe iterations or writing boilerplate code now flow toward strategy, review, and AI-specific work that simply didn't exist two years ago. Understanding that redistribution is the key to building a realistic budget — and to evaluating whether a proposal is thorough or dangerously thin.
For more from the show, check out the episode Bottom-Up Web Development: Building Accessibility In From the Start — a natural companion to the compliance and accessibility themes raised here.
Software and web development from the side that has to ship it and then live with it. Architecture decisions with a cost attached, scoping, technical debt, hiring and vendor selection, and the AI tooling question every engineering team is now answering whether they planned to or not.
Each episode takes one decision — rewrite or refactor, framework choice, build versus buy, how to scope a fixed-bid project honestly — and works through the tradeoffs, including the ones that only show up in year two. Written for engineering leads, technical founders and the people who fund them. Five or six minutes, no hand-waving.
Topics include rewrite versus refactor, build versus buy, scoping fixed-bid work honestly, technical debt you should keep, framework and platform choices, hiring and vendor selection, code review culture, and where AI tooling actually helps.
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