This is the question I asked myself this morning after several successive failures of RAG (Retrieval Augmented Generation) on my rental contract PDF file uploaded in Google Notebook LM – Free Version.
This was not a “tricky” question at all, just to clarify a point that many tenants may have as well.
The same hallucination was repeated in 3 different notebooks with the same document despite prompt reformulations.
Even more interesting: I emphasized the mistake to the tool in the next 2 prompts, but it persists on the veracity of its answer each time. In short, I haven’t found how to correct the situation…
I thought of a sentence that was not visible in the PDF, but a quick extraction of the raw text with PyPDF2 seems to confirm that this is not the case.
To answer it, a little “mise en abyme”: Let’s ask Google Gemini (2.5 Flash model) itself what it thinks. Let’s confirm it soon 😊
Gemini-generated report #
My prompt was : Assuming that generative AI doesn’t live up to its promise within 6 months to 1 year, and that user companies lose interest, what are the 3 possible scenarios for the IT industry in economic terms?
As an answer, the full report generated by Gemini 2.5 Flash on July 18th, 2025 is shared here in English
I initially generated a French version that I used for summarize section content. Please note to see this French version mention RPA (Robotic Process Automation) as a relevant alternative possibility for Business to Gen IA, but not mentionned it at all in English version…
Gemini-generated report summary #
Report Summary: Economic Scenarios for the IT Industry in Case of Generative AI Disappointment
This report analyzes the economic implications for the IT industry if generative AI fails to deliver on its promises within the next 6 to 12 months, leading to disinterest from user companies. The generative AI market, estimated at $37.89 billion USD in 2025 and projected to exceed $1 trillion USD by 2034, has attracted massive investments (over $100 billion USD in VC funding for AI in 2024, of which $45 billion USD for generative AI). This concentration of capital and the deep integration of generative AI across various sectors create systemic risk in case of failure.
The historical context of tech hype cycles, such as the dot-com bubble burst (78% NASDAQ drop, $5 trillion in losses) and “AI winters” (periods of reduced funding due to unfulfilled promises), offers crucial lessons. These precedents show that companies with solid business models and a focus on tangible value are more resilient.
A failure of generative AI would have immediate financial repercussions: massive asset depreciations, a collapse of the AI venture capital market, and a sharp decline in demand for specialized hardware (GPUs). Corporate IT budgets would be re-evaluated, exacerbating existing technical debt. The tech job market would see layoffs in specialized generative AI roles, but persistent demand for fundamental AI, cloud, and cybersecurity skills.
Three economic scenarios are considered:
-
The Recurrence of AI Winter: The most pessimistic scenario. Generative AI’s failure triggers a widespread and deep loss of confidence in all AI, leading to a severe contraction of the AI market, a drop in venture capital investments, mass bankruptcies, and widespread layoffs. AI innovation would be stifled in the long term.
-
Strategic Reprioritization: A more nuanced correction. Investments shift away from speculative generative AI towards more established IT domains with proven ROI, such as cloud computing, cybersecurity, data analytics, and robotic process automation (RPA). Generative AI companies might pivot towards more specialized, problem-specific AI applications. The job market would reconfigure towards these growing areas.
-
Fundamental Reset: A period of deep realignment. Generative AI’s disappointment prompts the industry to focus on strengthening its foundations: reducing technical debt, improving data quality and governance, and investing in practical, targeted AI applications (AI agents). The investment environment would become more conservative, and the job market would favor fundamental skills in software engineering and architecture.
Want to react ? #
Feel free to contact me by email contact(at)candle8.app or on LinkedIn.