Work🛠

In this model, AI autonomously schedules staff based solely on customer demand, using real-time data on peak times, expected foot traffic and historical patterns. Shifts are assigned dynamically, often with minimal notice, to ensure maximum operational efficiency and revenue.

This approach prioritises financial performance and employees are rewarded when the café achieves high productivity and customer satisfaction. Key features include extrinsic financial incentives, a contract-based relationship between employees and management, and an emphasis on distributed economic gains through operational efficiency.

This approach allows the AI to take into account employee preferences and availability when creating schedules, aiming to balance individual needs with operational requirements. Employees can indicate their preferred shift times, preferred days off and weekly hours, giving them greater autonomy and job satisfaction.

Here, AI delegates tasks that don't interfere with employees' ability to make decisions, exercise autonomy or engage in meaningful customer interactions. This 'capability-based' approach emphasises purpose, meaning and personal development, drawing on principles from motivational theory that focus on autonomy and capability building.

Employee-Centric Scheduling could be supported by the capabilities approach, which was developed by philosophers like Martha Nussbaum and Amartya Sen, and focuses on expanding individual freedoms, opportunities, and meaningful engagement in life. For Option B, this approach emphasises the importance of enabling employees to exercise choice, express preferences, and pursue purpose and meaning in their work. The AI, then, would be designed to support these values by balancing operational demands with respect for employees’ preferences and needs.

The capabilities approach promotes a more human-centered workplace, aiming for employee satisfaction, autonomy, and personal growth. By valuing employees’ well-being and supporting their ability to shape their work environment, it fosters loyalty, engagement, and potentially long-term retention. This approach can lead to a positive workplace culture that attracts talent and enhances organisational resilience.

However, prioritising individual capabilities may sometimes come at the cost of immediate efficiency or profitability. Additionally, the approach could make it harder to implement strict efficiency-driven practices, which some business models rely on for competitive advantage.

Demand-Drive Scheduling could potentially be supported by Contractarianism, a theory that is rooted in the idea that social arrangements should be understood as contracts in which all parties agree to certain terms, typically to maximise mutual benefit. In the context of Option A, the contractarian approach frames the employer-employee relationship as a transactional agreement, where employees agree to work under conditions that maximise efficiency and profit, and in return, they receive wages and bonuses for peak performance.

This approach prioritises business objectives, ensuring high productivity and financial performance, which can benefit all parties in the form of bonuses or improved job security if the business thrives. By viewing the employment relationship as a contract, it focuses on clear, measurable outcomes, which makes it attractive to businesses aiming for operational efficiency.

However, this form of Contractarianism can lead to a narrow focus on profit, often disregarding employee well-being or work-life balance. The lack of consideration for individual needs may result in employee burnout and dissatisfaction, which could erode morale and increase turnover. Additionally, a purely transactional relationship limits deeper investments in the workforce, potentially reducing employees to "tools" in service of the business's goals.

Imagine a world where AI has automated many forms of labour. From manufacturing to healthcare to hospitality, intelligent machines manage production, services, and even problem-solving. In this future, most people are freed from the economic necessity of work, and instead, society encourages play, creativity, learning, and social engagement. Education shifts from job preparation to personal development, focusing on curiosity, philosophy, art, and self-expression. With resources produced and distributed by AI, there’s less focus on economic status and more on community, relationships, and intrinsic fulfilment.

However, this future poses a profound question: Can a life without work still offer purpose? Work has traditionally provided structure, identity, and accomplishment, but without it, will people feel aimless or disconnected? Some may thrive, using their newfound time to pursue passions and relationships, while others may find it challenging to replace the sense of contribution and achievement that work once provided. This vision prompts us to consider what a meaningful life looks like when survival and productivity are no longer the main drivers.

In this future, AI and humans have fused into a new “cyborg” workforce. Advanced neural and physical implants integrate AI into the human mind and body, amplifying abilities and allowing people to process information, solve problems, and perform tasks with unprecedented efficiency. Work has evolved; rather than routine labour, people engage in complex, high-skill roles where AI enhances human creativity, intuition, and knowledge. In medicine, research, and the arts, AI provides the tools to expand human potential, creating a society of enhanced thinkers, creators, and doers.

Yet this future raises critical questions about autonomy and identity: If AI is deeply integrated into our minds and decisions, are we still fully “human”? Does this fusion enrich our lives, or does it risk compromising our sense of individuality? There is also the potential for inequality, as only some people may access the most advanced enhancements. In a world where humans and AI are interdependent, this vision challenges us to rethink autonomy, agency, and the boundaries of human identity.