It’s impossible to ignore the groundswell caused by artificial intelligence: transformations are accelerating in all sectors, boosting productivity and innovation… but also inequality. With a market value estimated at nearly $4.8 trillion in 2033, compared to $189 billion ten years earlier, AI is emerging as the beating heart of the next industrial revolution. Yet this ecosystem remains largely driven by around a hundred players (Apple, NVIDIA AI, Microsoft Azure AI, OpenAI, DeepMind, Google AI, IBM Watson, Anthropic, Meta AI, Huawei Cloud AI, Baidu AI, etc.), concentrating almost all R&D and workforce resources. Between the prospects of massive automation—impacting nearly 40% of global jobs—and the urgent need for workforce retraining, the central question is no longer “should we adopt AI?” but rather “who will benefit, and with what safeguards?” Debates and strategies built around access to infrastructure, data control, and skills development are becoming crucial to avoid replicating the digital divide on a large scale. The challenges of inclusive, ethical AI that serves human development will be at the center of this overview, enriched with concrete examples and the best recommendations for understanding its place in the arena of tomorrow. For a complete overview, see also this detailed article.2025 Overview: Concentration, Opportunities, and Risks of AI The global AI market is exploding, widening gaps as much as it is closing them. By 2025, the sector’s giants will dominate the technical scene: the budgets of Microsoft Azure AI, DeepMind, and OpenAI alone are comparable to the GDP of several African countries. This overconcentration raises a twofold question: which country or player is truly driving the AI revolution? And how can we ensure that the fruits of this technological race benefit more users than just shareholders? To illustrate this evolution, imagine Lila, an independent data scientist in Senegal. Faced with the power of NVIDIA AI or Google AI, she must multiply open source alternatives, as proposed by the dynamics of open innovation or infrastructure sharing. Without this, most local innovators could remain on the margins. This year’s forums and conferences, such as the AI Summit France 2025, highlight the urgent need to engage countries that are “absent from the table” so that AI doesn’t become a closed club reserved for Silicon Valley or Chinese tech. The most striking examples include the proliferation of MOOCs using IBM Watson to reach students in rural areas, or community hubs powered by Meta AI, which promote access to training for all.Automation and Productivity: What Gains, What Losses?
The impact on employment is impossible to ignore. According to the latest analyses, 40% of the world’s jobs could radically evolve thanks to AI, if not disappear altogether in some cases. The example of a logistics center massively switching to Baidu AI illustrates how productivity is soaring… at the cost of a complete redefinition of skills requirements. Countering the negative effects requires building genuine retraining and support programs. Where robotization was a concern, AI requires a bet on continuing education from the outset.
Huawei Cloud AI also offers hybrid solutions: co-pilot AI for industrial optimization, complemented by a training component for technicians and operators. This approach is gaining ground, because any serious AI policy must invest in skills—or risk seeing unemployment rise at the same rate as algorithms. Countries that are most advanced in reskilling, such as South Korea and Canada, are already sharing their successes on open innovation platforms, encouraging the adoption of these practices elsewhere.
Equity and Governance Issues: AI, for Whom?
While the promise of AI is immense, the distribution of economic and social benefits remains a major challenge. Of the 118 countries often absent from international discussions, many struggle to attract talent and investment due to a lack of adequate infrastructure. Establishing more inclusive governance is not just a slogan, but a strategic necessity. Global think tanks emphasize three key levers: strengthening digital infrastructure, democratizing access to data, and providing massive training for the AI era.
The trend toward open source is growing: Anthropic and Google AI are encouraging the publication of public datasets, allowing small teams to develop locally adapted solutions. In Madagascar, a medtech startup team is leveraging IBM Watson APIs, combined with open data feeds, to develop a predictive medical monitoring system for remote rural areas. South-South cooperation initiatives, inspired by Meta AI models or open source platforms, demonstrate that it is possible to break the glass ceiling—provided the right investments are made.
The alignment of national and international policies on these issues is booming. New frameworks inspired by ESG (Environmental, Social, and Governance) are emerging, forcing large companies and governments to transparently publish their AI impacts. A shared infrastructure, managed globally, could allow less-equipped players to access computing resources—and thus bridge the technological divide.
Nevertheless, there remains a real challenge for open innovation mechanisms to not only serve communication purposes, but also produce concrete results. The best field feedback comes from experiences like that of an African incubator that partnered with Microsoft Azure AI to offer shared GPU hours to local developers: in six months, productivity and project quality skyrocketed. Ultimately, this type of initiative will need to be sustained and expanded to turn the promise into reality, otherwise the ecosystem will close in on itself.