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Vibe coding has not been shown to wipe out software agencies. It has changed which parts of software work can be produced quickly—and made discovery, engineering judgment, security, and post-launch responsibility more visible. The strongest case for a changing agency model is not that clients no longer need expertise, but that expertise is worth less when sold only as hours spent writing code.
What “vibe coding” means—and what it does not
The phrase is used loosely. The Associated Press reported that Andrej Karpathy coined “vibe coding” in February 2025 for a style of coding in which a person leans into the feel of the result and pays less attention to the code itself. That is not the same as every professional developer using an AI coding assistant. The AP distinguished consumer-oriented natural-language app builders from tools that assist experienced developers.
In the same report, Anthropic Claude Code project manager Cat Wu said, “We definitely want to make it very clear that the responsibility, at the end of the day, is in the hands of the engineers.” The key distinction for a client is therefore not simply whether AI wrote code, but who can specify, inspect, and take responsibility for the working system. Associated Press, September 29, 2025.
Gartner analyst Philip Walsh, also quoted by AP, warned that treating vibe coding as a way for nontechnical people to produce business-ready software misunderstands its limits. That is an analyst’s assessment in a news report, not a universal test result. It is still a useful caution against equating a working-looking demo with a dependable business application.
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Does AI make software work faster?
Sometimes, but the evidence does not support a single productivity multiplier that applies to every project or agency. A 2026 state-of-the-art review by Dominik L. Michels and coauthors summarizes studies with materially different results: peer-reviewed field experiments reported 26% more tasks per week, independent randomized trials measured a 19% slowdown, and team telemetry found code-review time increased by 441%. Those findings use different methods and measures; none is an expected gain for an individual agency.
The review also describes strong code-generation capability alongside weaker fault detection and documentation that can be difficult to audit. That combination matters commercially: producing a first version faster does not necessarily reduce the effort required to understand, verify, explain, and maintain it. The review is available at arXiv.
Why a generated app is not yet a business system
A prototype can demonstrate a flow or help test an idea. A production system also has to behave predictably when users, data, integrations, and failures are involved. In a 2026 preprint, Siddhant Saxena, Nilesh Trivedi, and Vinayaka Jyothi evaluated six coding-agent application platforms across three domains and 18 evaluation cells. In that sample, no platform scored above 60% on engineering quality; none exceeded 65% on the authors’ security score against a 90% target; and concurrency handling was as low as 6%.
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These are results from the authors’ particular benchmark, not failure rates for all AI-built applications. The authors call for larger-scale replication. Their findings do, however, make a practical distinction: generating an application is only one part of delivering one that meets engineering and security requirements. See the benchmark’s scope and results in SWE-WebDevBench.
In a July 30, 2026 TechRadar Pro interview, Duda CEO and co-founder Itai Sadan argued that the difficult technical debt is often “everything required to run and maintain that application over time,” rather than just the code. He pointed to hosting, security, authentication, payments, infrastructure, and continuing maintenance. That is a vendor executive’s perspective, but the operational responsibilities he names are concrete items a client should assign before launch. TechRadar Pro interview, July 30, 2026.
What clients still need from an agency
The agency’s durable value is not measured only by how much code its team types. It can include understanding the business problem, turning it into a testable specification, choosing a workable design, coordinating stakeholders, and remaining accountable for delivery. Sadan summarized this view in the interview: “What an agency brings is the ability to translate business goals into digital results.” Because he leads a platform company that serves agencies, treat this as an attributed industry perspective, not independent proof of what every agency delivers.
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- Discovery and specification: Clarify who the software serves, what outcome matters, what is in scope, and how success will be checked.
- Architecture and integration: Decide how the application fits with existing services, data, permissions, and business processes.
- Quality and security: Review generated and hand-written work, test failure cases, and verify that access and sensitive data are handled appropriately.
- Communication and accountability: Keep the client informed, surface trade-offs, and establish who owns decisions and defects.
- Operations after launch: Assign responsibility for hosting, monitoring, updates, incident response, and maintenance.
AI can change how some of these tasks are performed or how much time a particular task takes. It does not automatically make the underlying decisions or obligations disappear.
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The provocative claim is plausible as a business argument if “old model” means selling implementation effort as the product. When tools can produce some code or prototypes quickly, clients have reason to ask what judgment, assurance, and ongoing ownership they are buying in addition to implementation. Agencies that can define outcomes and manage the whole system have a clearer value proposition than those whose offer is only a block of coding hours.
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But the available evidence does not establish that software agencies have disappeared, that vibe coding caused a market-wide collapse, or that it has reduced agency employment, prices, or margins. Nor is there a neutral, comprehensive head-to-head study here comparing traditional agencies with AI-assisted ones. The careful conclusion is narrower: AI changes the production process, while the economic and organizational consequences for agencies remain unquantified by these sources.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose an agency for an AI-assisted project
Whether a provider uses AI is less informative than whether it can explain how the work will be specified, checked, and supported. Ask for clear answers to these questions before agreeing to a scope:
- What outcome are we building toward? Ask how business goals become requirements and what acceptance criteria will show the work is complete.
- What is generated, and what is reviewed? Ask who checks code, tests behavior, documents important decisions, and resolves defects.
- How will the system fit our environment? Confirm integrations, data access, authentication, payment handling if relevant, and expected load are addressed.
- Who owns production operations? Put hosting, monitoring, security updates, backups, incident response, and ongoing maintenance into the agreement rather than assuming they are included.
- What happens when the project changes? Establish how new requirements, failures, and handoff to another team will be handled.
A useful provider should be able to discuss these responsibilities whether its developers use AI extensively, selectively, or not at all. The client is buying a dependable result and a defined line of responsibility—not a particular coding aesthetic.
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