Futures

Generative AI in 2023: Hype, Setbacks, and Future Challenges Ahead in 2024, (from page 20240121.)

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Summary

The year 2023 is viewed as the peak of generative AI hype, particularly with the rapid adoption of ChatGPT. However, expectations for transformative productivity and advancements toward artificial general intelligence (AGI) will face setbacks in 2024. Generative AI’s propensity for generating false information and hallucinations will be more evident, leading to a reassessment of its capabilities and potential. While businesses will increasingly adopt this technology, it may result in moderate automation that displaces workers without delivering substantial productivity gains. The dominance of major players like Google and Microsoft/OpenAI will likely lead to misinformation and mental health concerns due to increased screen time. Regulatory responses will emerge, but meaningful action may lag as the government struggles to keep pace with technological advancements.

Signals

name description change 10-year driving-force relevancy
Generative AI Disillusionment Expectations of generative AI’s capabilities are found to be overly optimistic, leading to disillusionment. Shift from high expectations of generative AI to skepticism and recognition of its limitations. In 10 years, generative AI may be more accurately integrated into workflows with realistic expectations. The gap between AI hype and actual performance drives a reassessment of generative AI’s role. 4
Misinformation Surge Increased use of generative AI in social media leads to a rise in misinformation and manipulation. Shift from information sharing to a landscape dominated by misinformation through AI-generated content. In a decade, society may develop robust systems to filter and verify AI-generated information. The competitive pressure on platforms to maximize engagement fuels the spread of misinformation. 5
Job Displacement vs Productivity Generative AI results in job displacement without significant productivity gains, challenging traditional business models. From optimistic views on productivity to recognizing generative AI as a source of job displacement. In 10 years, businesses may better understand human-AI collaboration, minimizing job displacement. Companies’ struggles with effective implementation of AI technologies drive this realization. 4
AI Regulation Delays Regulatory efforts lag behind the rapid development of generative AI technologies, creating governance challenges. Shift from calls for regulation to a realization of the slow pace of effective governance. In a decade, we may see more proactive regulatory frameworks addressing AI complexities. The fast-paced evolution of AI technologies outstrips the ability of policymakers to respond. 3
Foundation Model Duopoly The rise of a few dominant players in AI, limiting competition and innovation in the sector. From a diverse startup ecosystem to a duopoly dominated by a few major tech companies. In 10 years, the industry may consolidate further, impacting diversity in AI application development. The need for companies to leverage significant foundation models drives reliance on major players. 4

Concerns

name description relevancy
AI Hallucination and Misinformation Generative AI is prone to producing false information and ‘hallucinations’ that can mislead users and undermine trust. 5
Displacement of Jobs As companies adopt generative AI, many workers could be displaced without significant productivity gains, leading to unemployment issues. 4
Inequality The uneven adoption and benefits of generative AI may exacerbate economic inequality, with tech giants reaping the majority of rewards. 4
Deterioration of Democracy The uncontrolled rollout of AI could manipulate public opinion and misinformation, posing risks to democratic processes and civic engagement. 4
Mental Health Issues Increased screen time driven by generative AI usage may contribute to rising mental health issues among users. 3
Regulatory Lag Regulatory bodies are falling behind technological advancements, delaying necessary oversight and protection measures. 5
Antitrust Challenges Efforts to regulate monopolistic practices in the tech industry may face insufficient action, leading to increased market concentration. 4
Dependency on Foundation Models Growing reliance on major AI models from tech giants could stifle innovation and create vulnerabilities if these models underperform. 4

Behaviors

name description relevancy
Recalibration of Expectations As generative AI hype dies down, there will be a shift towards more realistic expectations about its capabilities and limitations. 5
Recognition of Hallucination Issues Increased awareness of generative AI’s propensity for false information and hallucinations will lead to calls for more responsible usage. 5
Focus on Augmentation and Training Needs Businesses will start identifying specific human tasks suitable for AI augmentation and the necessary training for workers. 4
Critical View on AI’s Cognitive Capabilities A growing number of individuals will question the feasibility of achieving complex human cognition through AI models based on word prediction. 4
Concerns Over Misinformation and Manipulation The rise of generative AI will exacerbate issues of misinformation online, leading to greater concerns about digital manipulation. 4
Increase in AI Startups and Open Source Models The emergence of more AI startups and open-source models will continue, though dominated by major players like Google and Microsoft. 3
Calls for Antitrust and Regulation Intensified discussions around antitrust actions and regulations in response to the dominance of major tech companies in the AI space. 4
Heightened Attention to Mental Health Issues The increase in screen time due to generative AI usage will lead to greater awareness of associated mental health problems. 3

Technologies

name description relevancy
Generative AI A technology enabling the creation of content through AI, offering productivity improvements but prone to inaccuracies and hallucinations. 4
Large Language Models (LLMs) AI models designed to understand and generate human-like text, significant for various applications but facing challenges with false information. 4
Artificial General Intelligence (AGI) The hypothetical ability of AI to understand, learn, and apply intelligence like a human, still seen as a distant goal. 3
Open Source AI Models AI models developed and shared openly, increasing competition but facing challenges against dominant players like Google and Microsoft. 3
AI in Social Media and Online Search Utilization of AI to enhance user engagement and targeted advertising, raising concerns over misinformation and mental health. 4
Regulation of AI Technologies The increasing call for policies to manage AI deployment and its societal impacts, highlighting a lag in governmental response. 5

Issues

name description relevancy
Generative AI Hallucination Awareness of generative AI’s tendency to produce false information and hallucinations will grow, revealing limitations in its reliability. 5
Economic Displacement and Productivity Gaps Generative AI’s adoption may lead to worker displacement without significant productivity gains, prompting reassessment of its role in the workforce. 4
Misinformation and Manipulation Online Increased use of generative AI in social media will exacerbate misinformation and manipulation, affecting public discourse and mental health. 5
Market Duopoly in AI The dominance of major players like Google and Microsoft/OpenAI may stifle competition and innovation in the AI sector. 4
Regulatory Lag in AI Governance The US government’s slow adaptation to AI advancements will raise calls for regulation, highlighting the gap between technology and policy. 5
Existential Risks vs. Mundane Risks of AI Discussions will shift from existential risks of AI to more immediate and practical risks affecting jobs, inequality, and democracy. 4