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In the high-stakes world of artificial intelligence, where fortunes are won and lost on the promise of tomorrow, British-Indian start-up Builder.ai captured headlines in 2023 by securing a staggering $250 million funding round led by the Qatar Investment Authority (QIA); one of the world’s largest sovereign wealth funds. But beneath the surface lurks a cautionary tale all too familiar: was Builder.ai be the next high-profile casualty in a landscape riddled with overvaluation, hype, and unproven business models? Our analysis uses Builder.ai as a powerful case study to analyse the perilous risks driving the current AI investment frenzy based on the experience of the Qatari wealth fund with Builder.ai, encouraging a sober reassessment of the dangers of uncalculated bets on some AI startups. Builder.ai used the Qatari investment and other money from different investors to develop plans develop an AI-powered software called Natasha, which helps project managers streamline breaking projects into manageable steps. The startup’s business model also centered on empowering non-technical people to build websites and apps without requiring any coding skills or technical expertise. enables non-technical people to build websites and apps, and platforms that allow programmers to use AI to write, edit and test code. Yet, cracks began to appear in the whole project, as unforeseen challenges and harsh market realities slowly unraveled the promise of this groundbreaking model.  Masking Manual Labor as Machine Learning Builder.ai’s reliance on human labor, staff and outsourced developers across countries like India and Ukraine, to perform the bulk of its work despite branding itself as an AI-driven platform highlights a critical issue examined in academic research: the overestimation of AI capabilities coupled with unsustainable business models. That fact wasn’t disclosed to the investors by its founder Sachin Dev Dugga , whom the BBC even interviewed him in 2024 as an AI expert. Ernst and Young further named him Entrepreneur of the Year in the UK the same year. One study, published in January this year under the title Analyzing the Potential for an AI Industry Collapse and Its Economic Implications, discussed the practice of AI-washing, a practice where companies overstate the role and sophistication of AI in their operations to attract investment and market attention while in reality depending heavily on traditional human processes. In the early phase, a startup like the collapsed Builder.ai might receive $450 million in funding based on promises of revolutionary AI capabilities. This inflow of capital leads to a rapid increase in the company's valuation, perhaps from $500 million initially (the sum wasn’t disclosed by the company) to its publicly declared valuation at $1.5 billion within a short period of time. Even though its revenues and profits remain minimal or nonexistent. A the technology fails to deliver immediate transformative results or profits, investor sentiment shifts. Funding dries up and valuations plummet. Auditors revised its previously reported sales figures, often downward, indicating that prior revenue reports were inaccurate or overstated. Consequently, a major lender, which was not named in the UK press, responded by seizing all of the company's cash reserves. This action is usually taken to recover outstanding debts or protect the lender’s interests in light of the company's deteriorating financial position. With its cash assets effectively frozen and unable to meet its obligations, the startup was compelled to file for bankruptcy in June in the UK.  The company booked $142 million in sales attributed to resellers who never actually paid for those sales. This practice inflates revenue numbers without generating real cash flow, misleading stakeholders about the company's financial health. Builder.ai claimed $107 million from customers who made deposits as small as $1. This indicates premature or inappropriate revenue recognition, as minimal deposits do not equate to legitimate sales or cash inflows. From 18% to 58% of Global VC in Just 2 Years When ChatGPT launched at the end of 2022, some 18% of global venture capital dollars flowed to AI and machine learning startups, according to data provider PitchBook. That number has more than tripled to 58% as of the first half of 2025. In the US and Europe alone, AI start-ups have received 38.4% of the $30 billion total in US and European value as of July 1 this year. 
● Share of Deal Value: This metric refers to the proportion of the total monetary value of all deals. ● Share of deal count: This metric means how many deals a person or company is part of compared to all deals happening. It counts the number of deals, not how big they are. The venture debt industry has been on a rapid growth lending a record $53.3 billion last year, up 94.5% from 2023. It’s been driven primarily by later-stage startups struggling to keep up with VCs’ growth expectations, and turning to debt to preserve their valuations. Now, it’s earlier-stage AI companies facing a similar problem, because because they can’t live up to the VC expectations, according to PitchBook analysis. Following the Builder.ai experience, the Qatari wealth fund has seemingly adopted a more cautious approach, now taking minority positions in startups. According to data obtained by Bloomberg, It’s typically holding less than 15% equity—and investing smaller amounts per deal, usually around $100 million or sometimes as little as $25 million. Nonetheless, the fund remains committed to AI investment, particularly in select software firms, as evidenced by its significant investment in Databricks Inc. in 2023, a company recently valued at over $100 billion, as detailed in the timetable below.  Rethinking Asset Lifecycles in the AI Hardware Market Rethinking Asset Lifecycles in the AI Hardware Market It’s noticeable that VC increasingly being extended to start-ups whose main assets are physical infrastructure (data centers, Graphic Processing Units). This trend may reflect broader investor optimism about AI market potential but also suggests elevated risk levels: if these startups fail to generate expected returns, their ability to service debt could become compromised. The main concern is about the short useful life of AI-related GPUs has significant financial implications, particularly regarding the amortization of these capital assets. Traditional capital asset investments like hardware are typically amortized over several years, often 7 to 10 years, reflecting their functional lifespan. If GPUs rapidly become obsolete within such a short timeframe, companies must accelerate amortization schedules, increasing the annual depreciation expense on their income statements, according to insights from an academic study titled: Industry 4.0 and life cycle assessment: Evaluation of the technology applications as an asset for the life cycle inventory. Accelerated amortization reduces book value of these assets faster, increasing costs in the short term and impacting reported earnings negatively. Increased CapEx and faster amortization reduce free cash flow, tightening budgets for other investments or operations. Despite the significant potential of the AI sector, it remains marked by considerable risks. Many companies within the industry are still in immature stages, and investing heavily in such a rapidly evolving field carries inherent uncertainties that stakeholders must carefully consider. |
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