No: the evidence does not show that vibe coding has wiped out software agencies. It does show why selling an agency as a team that simply writes code is becoming a weaker proposition. AI can help produce code faster in some settings, but clients still need someone to define the right system, check whether it works safely, connect it to the business, and keep it running.
What changed—and what did not
“Vibe coding” is a loose label for creating software by describing what you want in natural language and paying less attention to the code itself. The Associated Press reported that Andrej Karpathy coined the phrase in February 2025. The label covers different practices, from consumer app builders to professional coding assistants; it does not mean every developer has stopped inspecting code.
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That distinction matters for agencies. A tool can generate a screen or a working prototype without resolving what the business actually needs, how the new software fits existing systems, or who is accountable when something breaks. Anthropic Claude Code project manager Cat Wu put the responsibility with engineers: “We definitely want to make it very clear that the responsibility, at the end of the day, is in the hands of the engineers.” Associated Press, September 29, 2025.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThe available evidence does not establish a market-wide collapse in agencies, or quantify effects on agency jobs, prices, or margins. The more defensible conclusion is narrower: AI changes how some software work gets done, and it puts pressure on agencies whose value proposition is mostly hours spent producing code.
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Why productivity claims don’t settle the question
AI coding results vary with the task, the people using the tools, and the way productivity is measured. A 2026 state-of-the-art review by Michels and coauthors summarizes studies reporting different outcomes: peer-reviewed field experiments found 26% more tasks per week, independent randomized trials measured a 19% slowdown, and team telemetry showed code-review time increasing by 441%. These are findings from different settings, not a forecast or expected gain for an agency.
The review describes capable code generation alongside weaker performance in fault detection and documentation that can be difficult to audit. That does not mean all AI-generated code is poor; it means the speed of producing a first version is only one part of delivery. An agency still has to determine whether the result meets requirements and can be understood and maintained.
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A separate 2026 preprint, SWE-WebDevBench, evaluated six coding-agent application platforms across three domains and 18 evaluation cells. In that sample, no platform scored above 60% on engineering quality, and none exceeded 65% on the study’s security score against a 90% target. The authors also reported concurrency handling as low as 6%. They caution that the observations describe their sample and need larger-scale replication; these numbers are not universal failure rates for AI-built applications. SWE-WebDevBench and the review, Vibe Coding: Practice, Performance, Productivity, and Risk, are preprints.
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Where an agency can still earn its place
An agency’s durable value is not merely the ability to type code. It is the work of turning an ambiguous business goal into a reliable digital product—and taking responsibility for decisions and follow-through along the way. In a 2026 TechRadar Pro interview, Duda CEO and co-founder Itai Sadan described the agency role as translating business goals into digital results. That is a vendor executive’s view, rather than independent market research, but it points to work clients can evaluate directly.
- Requirements and scope: Identify the user, business outcome, constraints, and success criteria before building. A natural-language prompt can generate an answer; it cannot establish that the prompt captures the real problem.
- System design and integration: Decide how the application fits existing data, services, authentication, and workflows. A prototype may look complete while leaving these dependencies unresolved.
- Quality and security: Test behavior, inspect generated code, and assess risks before real users or sensitive data depend on it. The review and benchmark findings make this more than a ceremonial final check.
- Client coordination and accountability: Explain trade-offs, manage approvals, and own delivery against an agreed scope. The client should know who is responsible when requirements change or a defect appears.
- Operations after launch: Arrange hosting, monitoring, backups, security updates, and maintenance. Sadan’s interview stresses that the burden of AI-generated applications can lie in what is required to run and maintain them over time.
AI may accelerate parts of this work, but acceleration is not the same as eliminating the work. A useful agency engagement makes clear which activities are automated, which require human judgment, and who remains accountable.
How to choose an approach for a project
The right choice depends less on whether a tool can generate a convincing demo than on the consequences of getting the finished system wrong. Use these questions to distinguish a low-risk experiment from a production commitment:
- Is the goal exploratory? For a disposable prototype or internal demo with no sensitive data and no operational dependency, trying an AI builder may be a practical way to learn. Treat the output as a prototype until it has been independently checked.
- Will the system handle business-critical work? If it processes payments, controls access, stores important data, or must remain available, require explicit plans for security, testing, integrations, deployment, and support—not just a generated application.
- Are you extending an existing system? Changes to established software require understanding its architecture, behavior, and dependencies. A new-code demonstration does not prove that an agent can safely modify a particular codebase.
- Who owns the result after launch? Agree on maintenance responsibility, incident response, access to source code and infrastructure, and how future changes will be handled before delivery.
- Can the agency explain how it verifies the work? Ask what tests are run, who reviews the code and security, what limitations remain, and what evidence will be delivered. A confident demo is not a substitute for a verification plan.
What “the old agency model” means in practice
The old model is not every established agency; it is the version that treats billable production time as the primary value. When tools can generate more of a first draft, clients have reason to ask what expertise surrounds that draft: how the brief is shaped, how quality is demonstrated, and what happens after launch.
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The shift is therefore better understood as a change in what agencies must prove than as proof that agencies are disappearing. Agencies that can connect business goals to sound specifications, use AI without surrendering engineering judgment, and take responsibility for operation have a clearer case than those selling code volume alone. The available studies do not yet provide a neutral head-to-head comparison of traditional and AI-assisted agencies, so claims that one model has already won should be treated cautiously.
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