Modelling lock-in of AI adoption through skill erosion
Authors: Renaud Lambiotte
Organizations: Mathematical Institute, University of Oxford, Oxford, United Kingdom · Institute for New Economic Thinking, University of Oxford, Oxford, United Kingdom · Complexity Science Hub, Vienna, Austria
Generative AI can improve immediate performance but may erode the human skills required for future work. We study a model in which individuals tend to imitate successful peers, while their independent skills grow with practice and erode with reliance on AI. Because each agent's skill reflects their history, non-users and AI users develop different skill distributions, which we derive exactly. Skill erosion can produce bistability and hysteresis between a low-adoption, high-skill state and a high-adoption, low-skill state. When agents rarely change behaviour at random, the two transitions are asymmetric. A single AI user spreads through a population of experts only if AI-assisted work earns more than expert work, whatever the timescale of skill adaptation. A single non-user, by contrast, can lead an AI-dependent population back to independent work only if they regain enough skill before returning to AI, which becomes easier the more persistent behaviours are. As AI improves, adoption can jump abruptly, and a sufficiently long increase in its benefit, such as a period of low prices, can leave a population relying on AI after the benefit has returned to its initial value. Skill erosion thus creates a history-dependent switching cost that can make widespread AI adoption difficult to reverse.
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Figure 1: Feedbacks and individual memory. (a) Feedback structure. Reliance on AI erodes independent skill, and lower skill makes AI relatively more attractive. Crowding restrains adoption. (b,c) Illustrative skill trajectories of a single agent, coloured by behaviour, when behaviour changes often compared with the time skills need to adapt (b, ρ=0.1 ) and when behaviour is persistent (c, ρ=3 ). For these trajectories, switching is independent of skill and the rates are chosen so that the agent uses AI 40% of the time. The agent has the same mean skill in both cases (dashed line), but skill stays close to the mean in (b) and alternates between expertise and deep erosion in (c).
Figure 2: Same adoption and mean skill, different distributions of skill ( c=0.95 , a single stable state; b is tuned so that x=0.4 , and hence S=1−x=0.6 , in every panel). Histograms of skill in simulations of 2×104 agents, pooled over snapshots, and exact densities ( 7 ), for all agents and separately for non-users and AI users, as ρ increases. Each panel gives the adoption x and the mean skill S measured in the simulations, and the fraction of agents with skill below 0.2 predicted by the exact solution; simulations give 0.00 , 0.10 and 0.33 .
Figure 3: Bistability and hysteresis. (a) Stationary adoption as a function of the benefit b of AI-assisted work ( c=0.15 , ρ=0.39 ): exact solution (dashed on the unstable branch) and simulations of 104 agents in which b is slowly increased (circles) or decreased (squares). The arrow gives the width of the bistable window. (b) Phase diagram in the plane of the ratio of timescales ρ and the benefit b : folds of the exact solution for μ=0.02 (solid blue lines, bistable region shaded), folds measured in simulations (symbols), and boundaries of the bistable region for μ→0 (dashed, with ρ=2ε/ν ), at which a lone AI user invades a population of non-users ( b=1 ) and a lone non-user invades a population of AI users [ b=c+θ∗(ρ) , Eq. ( 8 )]. Below the bistable region, the only stable state has low adoption, and above it, high adoption; beyond the cusp, near ρ≈2.7 for μ=0.02 , adoption increases continuously with b .
Figure 4: Discontinuous adoption and lock-in ( c=0.15 , ρ=0.39 , simulations of 104 agents). (a) Adoption against the benefit b when b rises from 0.3 to 1.15 at a rate r , remains at 1.15 for 300 time units and falls back at the same rate, for four values of r , with the exact stationary solution (grey, dashed on the unstable branch) and the thresholds ( 8 ) for μ→0 (dotted). (b) Adoption over time in single runs in which b is raised from 0.7 to 1 for a duration D and then restored to 0.7 ; dots mark the end of the increase. Inset: fraction of 20 runs that remain at high adoption, against D , with binomial standard errors.
Repeated AI assistance can improve immediate task performance while reducing the skill available for future independent work. We develop a mathematical framework for this long-run tradeoff. The model tracks two state variables: a latent human skill level governing expected independent performance, and a delegation level representing the learner's evolving tendency to rely on AI. Skill changes through error-driven learning under practice and decay under delegation; delegation responds to observed performance, increasing when AI-assisted work appears to outperform independent work. We analyze the resulting dynamics and contrast them with fixed delegation. With fixed delegation, skill follows a one-dimensional learning-decay process with a single stable equilibrium. With adaptive delegation, the coupled system has two attracting equilibria separated by the stable manifold of an interior saddle. The existence and geometry of this separatrix require a global phase-plane analysis of the coupled dynamics. The system is path-dependent: small differences in initial skill or reliance can lead to different long-run outcomes. We use this characterization to show that AI assistance can improve short-run performance while producing worse long-run performance than a no-AI baseline. Increasing AI capability can enlarge the basin of attraction of the low-skill equilibrium, making delegation appear beneficial for longer while increasing the risk of eventual skill loss. The qualitative picture is observed to persist across alternative specifications. Together, these results show that the risk is not AI assistance itself, but the coupling between performance-driven reliance and use-dependent skill change.
Experimental evidence suggests that AI tools raise worker productivity, but also that sustained offloading can erode expertise. This creates a tradeoff when expertise is a complement to AI. To explore the consequences of this tradeoff, we develop a dynamic model in which a decision-maker chooses the intensity of practice-displacing AI offloading for a worker over time, trading immediate productivity against the erosion of worker skill. We decompose the tool's productivity effect into two components, one independent of worker expertise and one that scales with it. The model produces three main results. First, a decision-maker who fully anticipates skill erosion still rationally adopts AI when front-loaded gains outweigh long-run skill costs, lowering long-run productivity. The decomposition sorts deployments into five regions by their long-run effect, separating beneficial from harmful adoption. Second, the tradeoff introduces the potential for misaligned incentives to harm workers. When managers are short-termist or worker skill has external value, AI use can leave the worker worse off than with no AI, the outcome we call the augmentation trap. Third, when AI substitutes strongly enough for expertise, offloading can generate permanent divergence, with high-skill workers realizing their potential and low-skill workers deskilling. Small differences in managerial incentives can determine which path a worker takes.
At work, people develop skills through experience: novices acquire skills by completing tasks, while experts retain theirs through continued practice. AI assistance changes this process by offering workers a choice between solving tasks independently and accepting the AI's answers. While relying on AI saves immediate effort, it can also displace the practice needed to acquire and retain skills, including those required to oversee the AI. We extend an existing accept-or-solve model to a sequence of tasks in which workers' skills evolve through independent practice and reliance on AI. We compare myopic workers, who maximize immediate utility, with strategic workers, who also value their future skills. We show that novices can become trapped at low skill levels, while experts may lose skill, even as AI-assisted outcome accuracy remains high. Strategic workers, by contrast, may choose to solve independently even when AI is more accurate, recognizing the future value of acquiring and retaining their skills. We then study incentives that reward correct independent work and characterize the payments needed to encourage skill acquisition and retention. We show that independent work can become self-sustaining once workers acquire sufficient skill, eliminating the need for continued incentives. Counterintuitively, supporting a higher skill target can sometimes reduce long-run incentive costs, either by enabling self-sustaining independent work or by lowering the payments needed to maintain proficiency. Our findings thereby shift the focus from whether workers should rely on AI on individual tasks to how organizations can sustain human expertise over time, and which skills are worth investing in.
Moritz Diener, Daniel Hendriks, Philipp Spitzer +3
Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany · IBM Research USA