Artificial intelligence (AI) profoundly impacts economies by boosting productivity, fostering innovation, and transforming job markets. While it automates routine tasks and creates demand for specialized roles, it may also widen economic inequality and shift global competitiveness. AI supports sustainable development by addressing environmental, social, and economic challenges. It aids climate change mitigation, wildlife conservation, and waste management. In healthcare, AI enhances diagnostics, expands access, and accelerates drug development. In agriculture, it optimizes resource use and reduces food wastage. AI personalizes education, promotes inclusivity, and advances smart cities through efficient resource management. By fostering innovation in green technologies and enabling sustainable practices, AI plays a pivotal role in building an equitable, resilient future.

Artificial intelligence has already changed the way humanity lives. And that's just the beginning. Measuring the economic impact of AI is challenging. Well, it has already changed the way of production, of work, in several areas. I believe that the real impact will only be seen in the next decade.

AI is driving significant economic impacts by enhancing productivity, creating new job opportunities, and fostering innovation across various sectors. While it can displace certain jobs, AI also facilitates new roles in tech and data-driven industries. In terms of sustainable development, AI plays a key role in environmental sustainability by optimizing energy use, improving climate monitoring, and enabling precision agriculture. It also promotes social equity by enhancing healthcare access, education, and financial inclusion.

Needle in a needle stack: How AI causes semiotic inflation, which causes experiential devaluation

"While most discussions of generative AI center on issues such as algorithmic bias and disinformation, we should also consider the quantitative sea change brought about by these technologies. Large language models or LLMs can generate contents at a rate uncoupled from the human datasets they were trained on. Forecasts about such artificial outputs are difficult to make, but it seems clear that the word count and image/video bank of the internet will grow far beyond what humans actually produce. Although this growth may seem benign, I worry that it results in a semiotic inflation capable of devaluing many human experiences. What connects quantitative and normative considerations is scarcity. The basic idea is simple: When you discover a diamond, you become rich. When you discover two diamonds, you become twice as rich. However, when you discover a stash of diamonds so large that diamonds outnumber gravel, you become poor-and instantly make every diamond owner poorer too. Similarly, AI's vast output risks devaluing experiences that are vital to human flourishing. Adopting a wide evolutionary vantage that gives weight to proven cultural adaptations, I suggest that some situations have natural sign-to-object ratios. Hence, when users are flooded with too many signs, they can go against their long-term interests. Critics of AI typically cling to features that computers allegedly cannot mimic. Semiotic inflation, however, lets us grant the possibility of perfect AI counterfeits while still detecting them en masse, via their negative experiential effects. Hardcoded features like Bitcoin's 21 million token ceiling show that "[i]n contrast to the linguistic sign, the money sign cannot be reproduced in arbitrary quantity" (Bankov 2023: 117). Prompted by the rise of generative AI, I surmise that non-monetary signs also cannot be reproduced in arbitrary quantity. I thus draw parallels between healthy semiotic systems and the constraints governing viable monetary systems."

https://www.researchgate.net/publication/395327360_Needle_in_a_needle_stack_How_AI_causes_semiotic_inflation_which_causes_experiential_devaluation