E-Scan 39: Frictionmaxxing

E-SCAN 39: FEATURE

E-Scan 39: Frictionmaxxing

Police organisations, and wider society, are integrating Artificial Intelligence (AI) and digital tools to reduce workload, increase efficiency, and streamline decision-making. But in doing so, they may be removing something less visible, and far more important: friction.

In response, the concept of frictionmaxxing has emerged. Frictionmaxxing is a cluster of practices motivated by concerns around the integration of technology, particularly AI, into everyday life and work practices. The central concern regarding this integration is the impact it may have on ‘friction’. In this context the term ‘friction’ refers to the experience of effort, and the difficulties and challenges of goal-pursuit. Technology often aims to alleviate these difficulties and challenges by making tasks easier, more convenient, and ‘frictionless.’ However, while friction may cause frustration and annoyance, some view it as a basic feature of human experience, or at minimum an important feature of learning, motivation, and social relationships. Consequently, some wish to retain or even ‘maximise’ friction. 

The term ‘frictionmaxxing’ describes the practice of maximising task effort, by deliberately choosing less convenient options, which in practice means using less digital technology and usually avoiding the use of AI altogether. 

This Escan article covers four areas related to frictionmaxxing:

1. The concerns raised by frictionmaxxers.
2. What these concerns may mean for police work and use of AI.
3. Frictionmaxxing as part of a broader anti-AI movement.
4. The implications of such a movement on the policing operational environment. 

Consequences of removing friction

While it may seem odd to want to include or reintroduce friction into our lives, friction is key to many aspects of human life. The removal of friction has been shown to negatively impact both an individual’s cognitive development and their ability to form meaningful relationships.

As described by ANZPAA in 2025, the overuse or overreliance on Generative AI (GenAI) for task completion can cause skill erosion. Such erosion impacts memory recall and knowledge transfer. Emerging evidence has also found that when GenAI support is removed from some tasks, subsequent performance can worsen. The erosion of skills is particularly acute when friction is removed from tasks requiring higher-order processes like critical thinking. By overusing GenAI for tasks that would otherwise require these higher-order processes, a user removes friction but effectively ‘offloads’ or ‘outsources’ the key processes.

Effort and satisfaction, particularly regarding work, are also impacted by friction. The process of undertaking a task tends to make people feel more competent, and greater value is subsequently placed on the product of that work. Even when individuals work on seemingly meaningless tasks, the addition of friction increases perceptions of purpose and significance with the caveat that effort needs to be manageable. Conversely, the removal of friction shifts perceived value from work process to output. However, individuals place greater value on their work when processes rather than output product are rewarded and valued. This in turn increases tendencies to strive and persevere. 

Finally, greater reliance on GenAI for social connection may remove friction from interpersonal relationships and associated skills required to form them. For example, using personalised GenAI chatbots as social and romantic companions has become increasingly popular. One challenge with these uses however, is that chatbot ‘companions’ tend to sycophantically agree with their users and reinforce existing ideas. By contrast, human-human relationships involve disagreement and compromise. Moreover, building and maintaining relationships requires empathy, gained through attending to the needs, moods, and perspectives of others. These actions require effort, and therefore usually add friction to human interaction and connection. Without this friction, an individual’s social skills may be impacted by diminishing capacity for perspective-taking, unrealistic expectations that others will always be agreeable, and that conversations will always go the way they want. 

Although the abovementioned arguments may appear removed from policing, they carry clear dual consequences. As trust in institutions has weakened across Australia and New Zealand in more recent years, communication and social resilience will become even more critical policing capabilities, and the loss of these skills within policing needs to be minimised. 

However, even if police mitigate these internal risks, widespread reliance on GenAI across society is likely to make public interactions more contested, less predictable, and more emotionally charged.

Implications for police work and the use of GenAI  

Integrating software/apps and GenAI into police workflows and processes is clearly of some benefit, and it is unlikely that all integrations will generate negative consequences. The difficulty lies in identifying which integrations reduce friction in exchange for other, potentially worse, outcomes. 

Such identification is not always straightforward. Some tasks may appear as ideal for automation, but on closer analysis this may generate hidden costs and negative outcomes. For instance, GenAI is being used by some police organisations to draft incident/after action reports to reduce excessive administration. However, these reports contain a record of both suspect and police actions which helps provide an account of why an officer may have exercised discretionary powers. Given GenAI models will likely be trained on, and draw from similar incidents, GenAI drafted reports can contain predictive guesses of what the reasonable officer should have done in the situation rather than what the officer actually did. Over time these subtle shifts may distort understandings of appropriate practice. Relatedly, the act of writing a report may serve as a form of internal mental discipline because writing out an explanation for any exercise of discretionary powers can help reinforce legal limits of an officer’s authority. 

Police in the US have also complained that the use of predictive policing applications (such as crime hot-spot detection) undermines their on-the-job knowledge. This may reflect a fear that such experiential knowledge, gained through time and effort, is being devalued by AI integration, effectively eroding their knowledge domain and specific skills and expertise.  

Removing friction by automating some non-administrative but mundane and routine policing tasks has also been flagged as generating unintended consequences. Community policing initiatives are an important means of building police-public trust. Yet community policing often relies on mundane and routinised tasks, such as providing the public with non-emergency information or issuing tickets, to increase opportunities for interaction. Moreover, police organisations have increasingly made use of technologically mediated communications to engage with the public, which has arguably made engagement less personal and individualised, in turn impacting perceptions of trust and legitimacy. An antidote to less personalised and individualised communication are initiatives which increase the opportunities for face-to-face interactions, such as community policing approaches. Consequently, automation that removes opportunities for connection may not just cause a loss of trust but contribute to an ongoing decline in police-community relationships. 

Even if police organisations find the right balance between efficiency and friction, jurisdictions may not be immune to wider social impacts caused by removing friction. Police concerns regarding a lack of communication skills among new recruits are not new. However, as noted, the emergence of tools like Chatbots may erode other social skills, some of which are useful in policing, such as the ability to compromise and understand different perspectives. Without these skills, views and beliefs may become rigid and inflexible, making the exercise of discretion more challenging. Conversely, unrealistic expectations regarding levels of interpersonal agreeableness may cause disillusion with policing as a job following any tense confrontations with members of the public. 

Ultimately, frictionmaxxing reflects a broader anxiety about what may be lost as societies pursue increasingly frictionless systems. While AI and digital tools offer clear efficiencies, the removal of effort, reflection, and interpersonal challenge may also erode the very capabilities that support effective policing and social cohesion. Skills such as critical thinking, discretion, empathy, communication, and relationship-building are not simply outputs to be optimised, but capacities developed through repeated exposure to complexity, uncertainty, and effort.

For police organisations, this creates a difficult balancing act. The challenge is not whether AI should be adopted, but where and how it should be integrated without undermining the human skills and organisational cultures policing depends upon. In some contexts, reducing friction may improve efficiency and free up capacity. In others, friction itself may perform an important operational or social function, reinforcing accountability, professional judgement, and trust-building. The future challenge for policing may not simply be managing technological change, but identifying which forms of friction should be preserved, and why.

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