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1. More diversified access to large AI models

Since ChatGPT became available and accessible for widescale use in 2022, large language models (LLMs) have gained massive worldwide traction. For example, ChatGPT reached 1 million users in just a few days after its launch, prompting more diversified access to LLMs and acted as the catalyst for AI to be increasingly part of many daily workflows. Access to AI is facilitated by browser-based tools (ChatGPT, Claude, and DeepSeek) as well as integrations in services and products from vendors like Google (e.g. Docs) and Microsoft (e.g. Co-pilot, Outlook).

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Business models range from freemium tiers to pay-per-use APIs. Users now have the option to choose between cloud-based AI solutions or smaller, lightweight models running locally. Concurrently, open-source initiatives have contributed to the varied access to and democratisation of LLMs and tools. This development is reshaping how we write, code, research, and interact with AI, and even each other.

Public Values

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Autonomy
Privacy | Independency
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Justice
Transparency | Trustworthiness | Sustainability
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Humanity
Wellbeing

Maturity

WATCH
PLAN
ACT

Drivers

Automation & AI; Engineering advances & computation; Energy supply & power demand; Raw material scarcity

Impact

Education

  • The diversified access to AI presents both opportunities and challenges for educators by automating mundane tasks, allowing them to focus on teaching and personal guidance, while necessitating adaptation to an AI-driven educational landscape.
  • As students increasingly utilise AI chatbots for learning, productivity and wellbeing, there may be significant implications for traditional tutoring roles and skills like critical thinking. Amongst students, the digital divide might widen due to variations in knowledge, access and permission to use.

Research

  • In scientific research, AI can accelerate data analysis, enhance text interpretation, and support hypothesis generation. This serves to improve the speed and depth of scientific inquiry.
  • Challenges include concerns over copyright and intellectual property, the risk of disinformation in generated content, and the danger of researchers becoming over-reliant on AI tools. More proposals and papers are produced which increases the reviewing burden

Operations

  • Simplification of institutional processes by automating routine c.q. repetitive tasks, enhancing information management, and enabling smarter workflows.
  • Institutions have to navigate challenges such as potential copyright and data ownership issues, the risk of spreading disinformation, the reduction of human oversight, and concerns regarding the environmental sustainability of large-scale AI implementations.
  • Services and products enabling AI leading to unauthorised data processing/contract breach and DPIAs.