Builder.ai’s trajectory shows how an appealing narrative – “instant apps, powered by AI” – can attract capital and customers even if the technology isn’t real.
This article is based on the AI software development expertise of a custom software development company, Belitsoft. This agency confirmed its reputation with a 4,9/5 score from customers on the most credible review platforms (G2, Gartner, and Goodfirms). Belitsoft provides its clients with a precise project plan and cost estimation, whether it is a mature business, startup, or enterprise. Their experts help with building custom next-gen AI-powered solutions based on structured data analysis, natural language processing (NLP), speech recognition, computer vision, and more. Their AI developers prepare high-quality datasets, suggest optimal tech stack and AI models (tailored or open-source), and train AI models using AI algorithms on the top AI infrastructure.
Originally operating as Engineer.ai, the company pitched a one-click, AI-driven platform that could assemble mobile and web apps much faster and cheaper than traditional development. Behind the scenes, however, the “intelligent automation” was human developers in India completing tasks by hand. In August 2019, The Wall Street Journal revealed that the alleged AI was little more than a flashy interface on top of manual labor.
Within months, the firm rebranded to Builder.ai in an attempt to outrun the bad press.
Even after the exposé, Builder.ai kept courting investors with the same AI narrative. The Microsoft badge lent legitimacy, priming other funds to pile in. Over successive rounds, the company raised roughly $500 million and briefly claimed “unicorn” status at a $1 billion valuation.
Inside, the cracks widened. Tech leads were assigned 15–16 simultaneous projects, an impossible load for genuine oversight. Pricing appeared arbitrary – one client quote listed $46k for 29 features, another $56k for 43 features – numbers were tuned to match a target budget rather than reflect real effort.
What customers actually bought was a web-dev shop wrapped in AI marketing.
By spring 2025, the illusion could no longer be sustained. Cash burn, missed delivery dates, and dissatisfied clients converged, fundraising windows closed once investors realized the touted AI still did not exist. Builder.ai has shuttered.
AI software development platform bubble
Over the past few years, a pattern has taken root among “AI-first” startups. A charismatic founder unveils a slick demo and touts a “proprietary AI engine”, but behind the curtain, the product often rests on one of two crutches: inexpensive offshore labor or the same public large language model (LLM) APIs anyone can rent. The practice has a name – AI-washing.
1. The 10× Mandate
Venture funds must return the entire fund several times over, so incremental, cash-flow-positive businesses can’t move the needle. Investors concentrate on companies that might explode – even if the evidence is thin.
2. Charismatic Hype as Proof
Because explosive potential is hard to verify, sweeping visions, oversized TAM slides, and cinematic demo videos become proxies for traction. Founders who can perform excitement consistently out-raise more sober rivals.
3. Credential Shortcuts
Stanford CS diplomas, ex-FAANG badges, or a Y Combinator batch number reassure hurried investors that someone else has pre-vetted the team.
4. Network Gravity
When a marquee name – say, Microsoft or a storied Sand Hill fund – joins the cap table, a self-reinforcing message flashes: “smart money is in.” Follow-on rounds fill instantly.
5. Collision with Reality
Critics eventually publish exposés showing that fully autonomous agents or 90% cost reductions are mathematically implausible. Founders and backers wave away the math – breakthroughs are always “just months away.” But when the cash runway ends, tolerance for fake-it-till-you-make-it evaporates. Startups that never owned essential IP, never solved their cost structure, and never proved real demand unravel quickly. What remains are confused customers, marked-down venture portfolios, and a fresh wave of skepticism toward AI’s “next big thing.”
6. A Better Path Forward
Investors must insist on unit-level cost data and clear evidence that proprietary models outperform commodity APIs. Glossy videos are not traction.
What does proven, useful Gen-AI already deliver?
1. Data Gathering
Instead of armies of Playwright scripts, a large language model agent now roams the web, scooping up competitors’ prices in minutes. The information is fresher, the codebase is lighter.
2. Support Desk
When customers ask obscure questions, agents no longer scroll through 600-page PDF manuals. An OCR-plus-RAG layer surfaces the exact paragraph, complete with a citation, so answers come back in seconds.
For routine issues – and all the languages that pile in overnight – Gen-AI chatbots step forward, translating and troubleshooting while the human team sleeps.
3. Creative Studio
Generative tools turn ideas into polished visuals stripping hours of masking, compositing, and copy tweaks down to a handful of prompts, while text-to-video models such as Google Veo let marketers shoot “big-budget” ads for about $500 instead of six figures.
Lessons & warnings for AI software development buyers
Even though today’s machine learning tools can generate code snippets and polished screens in the time it takes to sip a coffee, the best digital products still succeed because of human ingenuity.
Seasoned engineers think beyond autocomplete. They design architectures that won’t buckle under real-world traffic, balance security and cost in ways no model can yet anticipate, and hunt down obscure, one-in-a-million failures that only emerge once users arrive. In short, AI is a powerful tool – but it’s not the craftsperson holding the blueprint.
When you’re about to buy – or build – something that relies on AI software development, the fine print counts.
Before you put your name on a contract, ask the vendor to fire up a live demo. Watch where the model actually runs, learn how it was trained, and see the moment it hooks into the rest of the system. Don’t settle for a polished sales pitch; talk to the engineers who wrote the code and can walk you through the quirks they solved along the way.
Next, dig into their track record. Ask for concrete examples of past projects: what the team built, how long each phase took, and – most telling – what business results the finished product delivered. A candid recount of real-world work separates a team that can ship from one that can only shine in meetings.
Finally, zero in on the price. The numbers should line up with the effort required. If you spot a mysterious “AI premium” tacked on without a clear explanation, treat it as a caution light. By seeing the technology firsthand, hearing directly from the builders, and matching costs to actual work, you’ll quickly tell solid AI software development expertise from empty hype.
The warning signs are usually obvious if you’re looking. Evasive answers to technical questions, a reluctance to disclose data-handling practices, or price tiers that spike without changes in scope should all prompt a hard pause. So should a slide deck packed with awards and influencer quotes – but suspiciously short on architecture diagrams.
In practice, the smartest strategy is a hybrid one. AI handles the boilerplate – the repetitive components and scaffolded screens – so your project moves faster. But keep experienced engineers in charge of the overall design, the tricky edge cases, and the ethical guardrails no off-the-shelf model can enforce.
About the Author:
Dmitry Baraishuk is a partner and Chief Innovation Officer at a AI software development company Belitsoft (a Noventiq company). He has been leading a department specializing in custom software development for 20 years. The department has hundreds of successful projects in such services as AI software development, healthcare and finance IT consulting, application modernization, cloud migration, data analytics implementation, and more for startups and enterprises in the US, UK, and Canada.
