What Builder.ai teaches us about AI washing and Tech investment
At the core of Builder.ai’s pitch was an enticing value proposition: shorten development cycles by 70% using artificial intelligence. Since 2016, its “AI” assistant Natasha was touted as a game-changing tool that could turn user inputs into applications, no coding required.
Customers were convinced. So were investors. Microsoft backed the start-up with capital and technical integration into Azure. Yet by 2024, signs of trouble began to surface: missed deadlines, quality issues, and increasing customer dissatisfaction.
Still, no red flags were raised, until an internal audit in 2025 uncovered that none of the promised automation actually existed. Each project was assigned directly to manual development teams in Gurgaon and Bangalore. The code produced by human hands was repackaged and falsely labelled as “AI-generated.”
To make matters worse, investigators also discovered a suspicious round-tripping scheme with VerSe Innovation, artificially increasing Builder.ai’s revenue between 2021 and 2024.
AI Washing: a growing risk for investors
AI washing refers to overstating or outright fabricating a company’s use of artificial intelligence to appear more innovative or scalable than it really is.
Three market forces make AI washing particularly dangerous:
- Hype-fueled valuations: companies using AI enjoy inflated valuations simply due to the buzz.
- Lack of technical transparency: without deep technical audits, investors often can’t validate what’s real and what’s not.
- Competitive pressure: start-ups oversell capabilities to gain funding or market traction, knowing verification is rare.
The risks extend well beyond the immediate financial fallout. AI washing obscures fundamental business realities, such as reliance on manual labor, poor scalability, and the illusion of proprietary technology. When the truth emerges, reputational damage is often irreversible.
Builder.ai didn’t just overpromise. It intentionally misled stakeholders by exploiting the knowledge gap between marketing language and actual tech infrastructure.
Why traditional Due Diligence isn’t enough
Builder.ai’s downfall is proof that standard due diligence practices no longer suffice when it comes to evaluating AI companies. Reviewing pitch decks, financials, and management claims simply can’t warn against sophisticated forms of AI washing.
Technical due diligence must evolve to effectively detect AI washing. It should encompass a thorough audit of the system architecture, a clear verification of the data lifecycle and the datasets used to train AI models, an examination of governance structures and process transparency, and, crucially, an independent validation of the actual level of automation achieved.
Vaultinum’s AI Maturity Audit is built precisely for this. It examines the technological reality behind AI claims, identifying companies that are genuinely innovating versus those simply applying existing tools, or worse, pretending to.
The new standard for investing in AI
Following the Builder.ai case, a new benchmark for responsible AI investing is emerging.
Investors must now require:
- Quantifiable metrics on AI automation vs. human input
- Traceability of datasets used in training AI models
- Third-party verification of proprietary AI components
In this context, technical audits from independent and specialised firms like Vaultinum are no longer optional, they are essential. Only a neutral third party can provide objective assessments free from commercial influence.
In Europe, regulatory pressure is growing, with more investment contracts expected to mandate verifiable AI performance indicators. Technical due diligence is rapidly becoming a baseline requirement for any deal involving artificial intelligence.
From scandal to strategy
The collapse of Builder.ai isn’t an isolated event, it’s a wake-up call.
As the AI investment landscape matures, the days of placing blind faith in AI-labeled start-ups are numbered. Private equity and venture capital firms that continue to fund AI ventures without thorough validation are assuming risks that go far beyond capital loss.
The antidote to AI washing is simple: technically rigorous, independent due diligence.
In a market where everyone claims to be using AI, only a deep dive into the code, architecture, and data can reveal who’s actually building the future—and who’s just painting it with the colors of innovation.
