Turing Trap

The Turing Trap is a concept in artificial intelligence (AI) and economics describing the risk of prioritising AI systems that mimic or substitute human intelligence over those that augment human capabilities, potentially leading to economic stagnation and missed opportunities for societal benefits.^[1] Coined by economist Erik Brynjolfsson, the term critiques the focus on AI that passes tests like the Turing test, which measures human-like behaviour, rather than fostering AI that enhances human productivity and creativity.^[1][2]

Background

Introduced by Brynjolfsson, director of Stanford's Digital Economy Lab, in a 2019 Daedalus article, the Turing Trap draws from Alan Turing's 1950 imitation game, the Turing test, which evaluates an AI's ability to mimic human responses.^[1][3][4] Brynjolfsson argues that AI's focus on tasks like speech recognition or autonomous driving—mimicking human skills—often overshadows tools that amplify human work, such as AI-driven analytics for decision-making.^[1][5] This mirrors historical technologies like computers, which transitioned from replacing typists to enabling knowledge workers via tools like spreadsheets.^[6]

Key arguments

The Turing Trap highlights several risks and distinctions:

• Substitution vs. Augmentation: AI that substitutes human tasks (e.g., chatbots replacing customer service agents) can reduce wages and jobs without proportional economic gains.^[1] Augmentative AI, like GitHub Copilot, which boosts programmer productivity by 55% according to studies, drives growth by complementing human skills.^[7][8]
• The Imitation Fallacy: The Turing test incentivises AI to deceive rather than innovate. Brynjolfsson contrasts chess-playing AIs (substitution) with recommendation algorithms (augmentation) that enhance user experiences on platforms like Spotify.^[2][9]
• Economic and Social Risks: Overemphasis on substitution exacerbates inequality, with low-skill jobs most at risk, while high-skill workers benefit unevenly.^[10] Critics like Emily Bender note that imitation-based AI can perpetuate biases in datasets, such as racial or gender prejudices, further complicating ethical deployment.^[11]

Escaping the trap

Brynjolfsson suggests strategies to prioritise augmentation:

• Redesign AI Goals: Shift from imitation metrics (e.g., Turing test success) to productivity and collaboration benchmarks.^[1]
• Education and Training: Invest in skills like creativity and critical thinking, which AI struggles to replicate.^[12]
• Policy Support: Encourage R&D for human-AI collaboration, as seen in tools like Adobe Sensei for designers or IBM Watson for drug discovery, which speeds research by 10x.^[13][14]

As of 2025, policies like the EU's AI Act emphasise augmentation to balance innovation and ethics.^[15]

See also

• Turing test
• Technological unemployment
• Human–computer interaction
• Erik Brynjolfsson