Simon Wardley, Researcher, Startup Advisor, and Former CEO, 0
Often, people assume that the AI story has already been written and that its outcome is settled. The answer is that it’s not. In truth, that’s far from reality. AI is not a single technology—it’s an expansive field made up of many distinct components. Some elements are advancing rapidly and being industrialized, while others are progressing more slowly, each finding its own particular use cases. At the core sits the foundational technology, upon which new use cases are continuously being constructed.
Emerging Practices and Use Cases
The evolution of these use cases depends heavily on emerging professional AI practices. It's no secret that they are still being developed. For instance, when discussing concepts like vibe coding, we’re referring to the process of creating deterministic code with the aid of AI, and then deploying it directly to production. This approach is different from simply using AI as a supportive tool in software development. The distinction is important: the applicability of different AI strategies depends on understanding one’s environment and knowing where specific methods fit. Our knowledge is still growing in this respect, making it clear that the contest for dominance or clarity in AI is far from over.
Also Read: Paris AI Summit 2025: Fostering AI Advances in Every Sector Possible
Bias and Sovereignty in AI
Alongside this technological evolution, issues of sovereignty are arising swiftly to the forefront. As we know, these AI systems are shaped by the data they’re trained on, which inevitably brings certain biases into play. Sometimes, those biases don’t align with the values or priorities of particular societies.
For example, many systems are trained heavily on market-oriented data and therefore tend to favor recommendations that lead to market benefits. If asked about educational investment, an AI might suggest implementing more AI tools instead of prioritizing aspects like critical thinking—something society values deeply.
In healthcare, an AI often leans toward approaches that support wellness or preventative medicine—market benefits—while societal benefits should include measures like patient-reported outcomes or open data sharing.
Control and the Power of Centralization
These built-in biases mean that the AI-powered systems reflect their own priorities, which aren’t always suitable for every society. As a result, national sovereignty concerns are emerging alongside the rise of AI. There are also deeper philosophical issues at play, such as the formation of almost “theocracies” around certain AI systems—a situation where the tools, platforms, and programming languages that facilitate AI’s industrialization give disproportionate influence to a small group of actors.
Future Directions and Opportunities
To visualize this, consider historical parallels: the printing press as the tool, paper as the medium, and written language as the means of communication. When those are controlled by just a few, the power over information and even societal values becomes concentrated in their hands. It's a profoundly influential position, and governments are likely to step in to address these sovereignty and control concerns.
Also Read: This 28-Year Old Leader Was Hired by Mark Zuckerberg to Lead Meta's AI Efforts
It's crucial to recognize that the AI landscape is still evolving. Open-source efforts—particularly in places like China—continue to make significant progress. On top of that, countless new practices and use cases are being developed, driving further growth and innovation. While some areas have made practical breakthroughs, the journey is ongoing. There’s much more to discover, refine, and implement. The field remains open, with new opportunities and challenges emerging all the time.
In healthcare, an AI often leans toward approaches that support wellness or preventative medicine—market benefits—while societal benefits should include measures like patient-reported outcomes or open data sharing.
Control and the Power of Centralization
These built-in biases mean that the AI-powered systems reflect their own priorities, which aren’t always suitable for every society. As a result, national sovereignty concerns are emerging alongside the rise of AI. There are also deeper philosophical issues at play, such as the formation of almost “theocracies” around certain AI systems—a situation where the tools, platforms, and programming languages that facilitate AI’s industrialization give disproportionate influence to a small group of actors.
Future Directions and Opportunities
To visualize this, consider historical parallels: the printing press as the tool, paper as the medium, and written language as the means of communication. When those are controlled by just a few, the power over information and even societal values becomes concentrated in their hands. It's a profoundly influential position, and governments are likely to step in to address these sovereignty and control concerns.
While some areas have made practical breakthroughs, the journey is ongoing. There’s much more to discover, refine, and implement
Also Read: This 28-Year Old Leader Was Hired by Mark Zuckerberg to Lead Meta's AI Efforts
It's crucial to recognize that the AI landscape is still evolving. Open-source efforts—particularly in places like China—continue to make significant progress. On top of that, countless new practices and use cases are being developed, driving further growth and innovation. While some areas have made practical breakthroughs, the journey is ongoing. There’s much more to discover, refine, and implement. The field remains open, with new opportunities and challenges emerging all the time.