The 5 AI development traps nobody tells you about

Drift without specifications, false green lights, opaque credits, disposable code and dependence: five documented traps encountered in our own measurement campaigns, and a safeguard for each.

5 traps recur in AI-led application projects: drift without specifications, false green lights, opaque credits, disposable code and platform dependence. We encountered all of them while measuring our benchmark campaigns. Each can be avoided through a requirement established before starting.

Trap 1: drift without specifications

The symptom: the fourth request breaks what the second built. The cause: what the product should do exists only in chat history, and an AI asked to add VAT does not know it should leave Tuesday's discount alone. The safeguard: requirements written before code and consulted for every change. Every development request should start with ‘what does the document say?’, and the document is corrected before the product.

Trap 2: false green lights

The symptom: everything displays ‘done’, nothing works. Our August campaign retains a textbook case: a submission rated 92.5 out of 100 on the strength of its documents did not compile and wiped its database on every launch. An AI agent can produce a convincing success report about failed work without lying anywhere: it describes what it intended to do. The safeguard: executed checks, never declarations. Insist on seeing tests run, or use a tool refusing to mark a stage complete without evidence.

Trap 3: opaque credits

The symptom: you cannot say in advance what a project will cost, and credits melt away even when generation fails. Most tools bill consumption without committing to an outcome; failure costs the same rate as success. The safeguard: published costs and a cap. We publish ours, measured on real projects: $115.65 for nine completed projects in our campaign, while the reference method spent $238.66 and exhausted its budget three times along the way. Ask every tool you evaluate for these figures; silence is an answer.

Trap 4: disposable code

The symptom: every enhancement seems to start from zero, and the developer consulted for an audit sighs. Code generated without architecture or conventions works on day one, then every addition weakens the whole because nothing was designed to last. The safeguard: foundations decided before construction, architecture established by a dedicated role, work broken into stages and review before committing. In short, the sequence of a directed project, where continuous conversation merely piles things up.

Trap 5: platform dependence

The symptom: leaving the tool means abandoning the product. The exit test comes down to three questions, detailed in our ownership article: download the code, run it elsewhere and own a document describing the rules. The safeguard starts on day one: choose a tool you can leave with your code and specification, and verify this before placing an important product in it.

The list to keep

Before entrusting your product to AI, demand five things in writing: requirements approved by you, checks executed before you, announced costs, foundations designed to last, and a possible exit with code and documents. The five traps share a root: producing has become easy while proving remains demanding; these five requirements put evidence back at the centre. That is the work Maestro organises, approval by approval.