Can AI Build My App?

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Ask an LLM to “build me a house,” and it may well return a four-walled box with a hole in one side labelled “door”. Turning it into something fit for purpose requires professionals who know why certain choices matter.

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Large-language models excel at recombining what they have already seen. Hand them a well-trodden pattern - a Kanban board, a skeletal news aggregator, a clone of last year’s SaaS hit - and most of the moving parts arrive in seconds. Push them into territory where examples are scarce and you quickly see the difference. The rarer the problem, the shallower the training data; the shallower the data, the looser the predictions.
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By definition, genuinely novel ideas sit at those under-sampled edges. That is where vibecoding falters, and why nothing with serious commercial upside can be vibecoded from scratch in 2025. If a prompt alone can recreate your idea, two uncomfortable truths follow:

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1. Your IP probably isn’t novel: Someone else can replicate it tomorrow.
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2. Your moat is paper-thin: Competing on price with flat-pack code is a race to zero.

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Where vibecoding does shine is in collapsing the distance between concept and first prototype. Early sketches that once took weeks of engineering time now appear in a single afternoon, giving entrepreneurs a quick proof-of-concept for investors or alpha users. Yet a prototype is only a sketch - the hard work lies in turning that sketch into a secure, compliant, cost-controlled and revenue-driving reality.

At that stage the spreadsheet of expenses begins to feature items no prompt can magic away: penetration testing, observability pipelines, smart database design, cloud-cost optimisation, maintenance and the rest.
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Daedalus meets clients precisely at that inflection point. Internally we integrate LLMs into our engineering workflows every day. The result is not a labour swap but a force multiplier. Augmented by AI, a lean core of senior engineers and scientists now outputs what once required fifty full-time heads. The budget line looks similar, but the composition has shifted. Expertise has not been devalued, but it has become scarcer and more consequential. Markets respond in kind. Where a Series A financing round might previously have underwritten ten mid-level developers earning £100k, today the same capital backs three or four highly specialised professionals at £250k apiece. The total cost is steady; the depth of knowledge, and the potential output per person, has doubled.

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Proponents of the “agentic” future often gloss over the human factors that make enterprise software viable. Boards remain wary of handing over sensitive data to an opaque black box with no liability trail. Regulators will resist systems whose internal logic is indistinguishable from statistical guesswork. Even where governance is satisfied, the cost curve can become punitive: many an AI-generated app that looks promising on paper reveals itself to have soaring server bills in month two because the model deployed everything in debug or spun up micro-services that never sleep. An LLM has no native concept of kilowatt-hours, ingress-egress fees or the impact of one poorly-indexed query across a million rows. Seasoned engineers do.

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The gap between “works on my prompt” and “powers a profitable business”, therefore, remains wide. Vibecoding can accelerate the first steps but cannot bridge the chasm alone. Crucially, it offers no help with the unknown unknowns, the silent edge-cases and legal tripwires that experience teaches us to anticipate. If you lack the vocabulary to query the machine, you cannot meaningfully evaluate its answers; and what looks like a shortcut can become an expensive detour or, worse, a dead end.

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An LLM is a powerful weapon, but, like all weapons, its utility depends entirely on its deployment - in skilled hands it can deliver huge benefits, but without said skill it can become a dangerous liability. The modern software organisation is already adapting: doing more with fewer people, but insisting that those people have a deeper command of architecture, security and product strategy than ever before.

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Contrary to what some are saying, we firmly believe that, at the top end, custom software products and platforms still endure and will still grow. The surge of enquiries we field at Daedalus underscores that companies still face large, non-linear problems demanding tailored solutions - supply-chain optimisation, privacy-preserving analytics, intelligent automation within tightly regulated domains. These challenges require judgment, negotiation with stakeholders, and an iterative understanding of what success looks like. They are not solvable by copy-pasting the past.

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What vibecoding threatens most directly are linear SaaS offerings whose only virtue is speed to market. Those can indeed be duplicated overnight, as can the revenue they depend on. Products that combine deep domain knowledge, sophisticated systems design and a defendable user experience, though, remain out of the reach of prompt-engineering alone, requiring partnership rather than substitution.

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To founders tempted by the flat-pack allure of the readymade solution, the house analogy is instructive. Buying a ready-made kitchen from IKEA is perfectly rational; expecting the same kit to erect a multi-storey apartment block is not - and even your kitchen needs someone who knows what they are doing to plumb in the sink. Skilled electricians and structural engineers still find themselves gainfully employed. The choice, then, is whether you wish to compete in mass-produced laminates or craft durable structures from Carrara marble. At Daedalus we build in marble.

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So by all means enjoy vibecoding as a creative sandbox. Spin up prototypes, test ideas, show your investors a clickable vision of tomorrow. But when the time comes to turn that vision into a business that scales safely, earns trust and generates profit, you will still need architects who understand both what the machine can do and, more importantly, what it cannot.
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‍Expertise has never mattered more.