In just a few months, generative artificial intelligence has gone from a laboratory curiosity to the next big technological frontier. Behind the excitement surrounding solutions such as ChatGPT (developed by OpenAI, backed by Microsoft) and DeepSeek (a Chinese startup aiming to rival the American giants), lie colossal challenges: profitability, sovereignty, workforce training, and even climate impact. This chronicle offers an overview of the key figures in AI, then explores three economic scenarios likely to shape the global landscape of this revolution.
1. Key Figures: What They Mean for Households and Businesses
To better grasp the stakes, the table below presents three main themes—productivity gains, employment, and energy costs—along with their real-life implications for individuals and companies.
| Productivity Gains: An Inexhaustible Gold Mine? | Jobs: 25% “Disrupted,” 12 Million “Created” | Energy Costs and Cloud Spending: The Great Challenge | |
|---|---|---|---|
| What the Figures Say | – McKinsey (2023) anticipates up to $4.4 trillion per year in added value from generative AI. – Across the EU, this could theoretically support a 2% GDP increase if widely adopted. | – Goldman Sachs (2023): up to 25% of jobs “disrupted” by 2030 (administration, customer support). – World Economic Forum (2023): 12 million new positions focused on AI systems design and maintenance. | – Synergy Research (2023): $500 billion in cloud investments by 2026, driven by AI. – Training a model (e.g., GPT-4) can emit hundreds of tons of CO₂ (University of Massachusetts, 2023). |
| Points of Comparison | – $4.4 trillion is more than Germany’s annual GDP (around $4 trillion). – For an SME, potential benefits could be seen in accounting, customer relations, and automating repetitive tasks. | – 25% is one quarter of the workforce in key sectors (accounting, telemarketing, etc.). – 12 million new jobs is almost the active population of countries like Belgium or Greece. | – $500 billion is more than double France’s total annual budget for Education and Research. – The carbon footprint of training a single AI model can equal thousands of long-haul flights. |
| Implications | – For households: potential drop in certain service costs (insurance, banking, legal advice), as AI can reduce operational expenses. – For businesses: need to invest in staff training and IT infrastructure to capitalize on these gains. | – For households: risk of unemployment for vulnerable profiles but opportunities for younger, data-savvy professionals. – For businesses: retraining and new professions (AI consultants, “prompt engineers,” etc.). | – For households: if servers increasingly consume energy, electricity bills or service fees (online hosting, for instance) could rise over time. – For businesses: sustainability becomes a major factor (cost and brand image), prompting a push to reduce data center consumption. |
2. From Promising Numbers to Economic Models: The Necessary Transition
The data above highlights the vast potential of generative AI while revealing significant disparities. Much like Amazon in its early days, neither OpenAI nor DeepSeek has found the holy grail of profitability yet, despite massive investments. One relies on platform effects (OpenAI is strongly tied to Microsoft Azure), while the other leans on cost-efficiency and government support (DeepSeek and the Chinese market).
In an ecosystem where data centers consume hundreds of billions of dollars and enterprise adoption may be slower than predicted, the central question becomes: how can generative AI be monetized effectively? The table showcases the magnitude of possible gains, but also the financial, human, and environmental costs. Recent tech history (Google, Facebook, etc.) reminds us that business models often emerge empirically, shaped by trial and error as well as partnerships.
In this vibrant context, three economic scenarios stand out—each offering a distinct path to turn innovation into sustainable revenue while addressing concerns of sovereignty, competition, and environmental accountability.
3. Three Economic Scenarios for the OpenAI-DeepSeek Rivalry
Scenario A: The “Google-Style” Advertising Model
- Principle: Provide free or freemium versions, leverage user attention, and monetize via targeted advertising (or user data sales).
- OpenAI might thus strengthen ChatGPT’s integration with search engines (e.g., Bing) or social media.
- DeepSeek, backed by Beijing, could favor a model less reliant on advertising, possibly state-subsidized to ensure data sovereignty and security.
Scenario B: The “Premium Licensing” and B2B Model
- Principle: Reserve advanced versions (GPT-5, GPT-6, etc.) for clients able to pay high subscription fees, such as banks, insurers, and large industrial groups.
- OpenAI would cover infrastructure costs by charging for exclusive access to its most powerful models.
- DeepSeek, for its part, might offer turnkey solutions to strategic national sectors (banks, hospitals, government agencies), capitalizing on an ecosystem less open to American providers.
Scenario C: Technological Breakthrough and Cost Reduction
- Principle: Simultaneously, advancements in AI hardware (dedicated chips) or training optimization (quantization, model distillation) could drastically reduce energy consumption.
- Democratization Effect: Much like personal computers in the 1990s, declining unit costs would open generative AI to a wider range of players (SMEs, emerging countries), lowering entry barriers and pushing OpenAI and DeepSeek to stand out via product innovation rather than sheer infrastructure capabilities.
A Revolution to Be Invented
Between promises of productivity and risks of polarization (impacting employment and resources), generative AI finds itself at a turning point for the global economy. OpenAI and DeepSeek are its most publicized faces, yet they only represent the tip of a vast movement affecting every sector and raising fundamental questions: Who will finance the transition? How will the benefits be distributed? What rules will govern the geostrategic and environmental facets of AI adoption?
The numbers speak to a major economic opportunity, but the history of tech pioneers (Amazon, Google, Microsoft…) shows that profitability rarely follows a simple or rapid path. The scenarios outlined here illustrate the diversity of possible approaches—each with its pros and cons. As AI becomes embedded in our daily lives, this Sino-American rivalry underscores the need to craft, sometimes from scratch, a sustainable model that is both profitable and socially responsible.
Main References
- McKinsey (2023).
The Economic Potential of Generative AI.
https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier - Goldman Sachs (2023).
Generative AI Could Raise Global GDP by 7%.
https://www.goldmansachs.com/insights/pages/generative-ai-could-raise-global-gdp-by-7-percent.html - Synergy Research (2023).
Cloud Market Reports.
https://www.synergyresearchgroup.com/ - World Economic Forum (2023).
Future of Jobs Report.
https://www.weforum.org/reports - University of Massachusetts (2023).
Estimating CO₂ Emissions of LLMs (Energy and Policy Considerations for Deep Learning).
https://arxiv.org/abs/1906.02243 - OECD (2024).
SMEs and the Adoption of AI.
https://www.oecd.org/going-digital/smes-and-the-adoption-of-ai/ - Reuters (2025).
DeepSeek and Its Disruptive AI Model.
https://www.reuters.com/technology/artificial-intelligence/what-is-deepseek-why-is-it-disrupting-ai-sector-2025-01-27/