Right now, AI is a mess - Strategian: Science Magazine

Definition of AI slop (from PCMag; image created by AI)

Background:

Right now, artificial intelligence is a complete mess–economically, socially, politically, culturally, environmentally, etc., and especially in the United States.

Is this just a sign of an immature technology that has been labelled and presented in a very confusing way? Presented as a monolith, when it is not that at all?

Maybe.

Is this a sign of an immature technology foisted upon society by huge tech corporations and billionaires mainly intent on profits, greed, and power?

Likely.

Or is this a sign of a technology for which the risks and real damage simply outweigh any benefits? Or is it, again, just too soon to tell?

But the result, right now, is a mess!

Sources:

*Cruz, N. (2026). Illusions of understanding from outsourcing thinking to LLMs. Computational Brain & Behavior. [PDF]

“Some illusions of understanding are an inevitable part of the research process, while others can be avoided or overcome by careful critical thinking and observation. We are facing an increased risk of avoidable illusions as more research activities are delegated to large language models (LLM). LLMs can be useful but they cannot think, and their use can undermine our thinking and understanding. Thinking for ourselves is hard and error prone but worthwhile – and there are no shortcuts to understanding.”

*Lakhtakia, A., & Lakhtakia, N. S. (2026). Is artificial intelligence a good servant but a bad master? In: Biologically Inspired Materials, Processes, and Systems (BIMPS) 2026 (Vol. 13944, pp. 80-96). SPIE. [Cited by]

“Artificial intelligence (AI) is rapidly moving from a specialized research topic into a widely deployed general-purpose technology, driven by advances in transformer-based architectures, convincing enough productivity gains, large private investment, geopolitical competition, and expanding educational adoption. AI comprises two distinct sets of methodologies: (i) predictive AI, which is grounded in statistical inference and aims to model measurable physical reality, and (ii) generative AI, which produces plausible synthetic content without intrinsic verification against physical reality. Whereas predictive AI can strengthen human decision-making through rigorously tested models grounded in impeccable mathematics, generative AI introduces systemic risks that extend beyond technical reliability. Generative AI is a wicked problem, because its consequences are deeply entangled across environmental, human, ethical, educational, and cultural domains. Generative AI accelerates energy demand, greenhouse-gas emissions, water consumption, and electronic waste, thereby amplifying environmental burdens. It also intensifies inequality through an expanding AI divide, undermines intellectual property norms, and relies on poorly compensated human labor for ongoing data annotation and content moderation. Furthermore, hallucinated outputs, misinformation, and AI slop degrade epistemic trust in online information ecosystems. Ethical dangers include privacy erosion, algorithmic bias in societal decision-making, and increased risk of catastrophic failures in high-stakes contexts such as policing and warfare. Generative AI may reduce critical thinking in students through cognitive offloading and weaken originality and writing skills. AI may serve as a useful servant to specialists for workflow acceleration but we must reject it for general-purpose use, with strict regulation necessary for public chatbot deployment to preserve human judgment and sustain civilization.”

*Shafik, W. (2026). The Dark Side of AI: A Human and Societal Perspective. Springer Nature. [Cited by]

“This book addresses how the growth of AI could undermine fundamental human values such as privacy, security, employment, and democracy. It emphasizes the critical challenges that arise when artificial intelligence technologies operate without proper ethical oversight or regulation. Key topics include AI’s potential for surveillance, the manipulation of media through deepfakes, autonomous weapons, and job displacement, as well as the growing concerns around algorithmic biases that may perpetuate social inequalities and harm marginalized communities. It offers insights into how AI can be integrated into society responsibly, while addressing the potential dangers that could threaten our future, making it relevant for those seeking to understand both the promise and peril of emerging AI technologies.”