Douglas Engelbart

The Psychological Stakes of the AI Development Boom Software engineering is currently serving as the "canary in the AI coal mine." As Jeremy Howard, founder of Fast.ai, argued during his keynote at AI Engineer Melbourne, we are at a critical juncture where the work we choose to do with artificial intelligence will either amplify human flourishing or diminish the human spirit. Drawing on 50 years of psychological research, specifically **Self-Determination Theory (SDT)**, Howard highlighted that humans are naturally born to be curious, agentic, and inspired. However, this spirit can be crushed when individuals reject growth and responsibility, leading to the apathetic, listless state often observed in modern workplaces. The core of this tension lies in the difference between **eudaimonia**—fully actualizing one's capacities—and **hedonia**, the pursuit of frictionless, passive pleasure. While many AI tools are marketed on the promise of hedonia (making tasks easier, faster, and more automated), Howard warned that true satisfaction comes from the effortful mastery of a craft. When we outsource the cognitive heavy lifting to AI without maintaining a connection to the underlying principles, we risk losing the very autonomy and mastery that make us feel vital and self-motivated. Dark Flow and the Illusion of Productivity A particularly insidious phenomenon emerging in the AI era is what Howard terms **dark flow** or "junk flow." While traditional flow state occurs when a high level of challenge meets a high level of skill, dark flow mimics this sensation through a superficial, addictive feedback loop. It is the dopamine hit of seeing an agent generate 200 lines of code in seconds, regardless of whether that code is actually correct or understood by the developer. This "vibe coding" creates an **illusion of control** that can lead to massive technical debt—what Howard described as "200,000 lines of vibe-coded software" that becomes nearly impossible to debug or verify. Prominent developers like Armin Ronacher (creator of Flask) and George Hotz have begun to voice similar concerns. The thrill of rapid generation is often decoupled from external validation. Developers feel incredibly productive in the moment, but when faced with quarterly metrics or the need to ship reliable, high-accuracy products, they realize they have actually moved slower. The pace of meaningful progress declines as the time spent on verification and debugging failures increases, leading to a state of being busy without being effective. The Rise of Supervisory Engineering and Flow Decline Annie Vella of Westpac New Zealand expanded on these psychological costs through her longitudinal research on professional software engineers. She identified a new category of labor: **supervisory engineering**. This work involves directing AI, evaluating its outputs, and managing the "middle loop" of development. While 84% of engineers reported feeling more productive with AI, a growing number—nearly 27% within six months—reported a decline in their overall **Developer Experience (DX)**. Vella's findings suggest that **flow state** is the most negatively affected dimension of DX, followed by an increase in **cognitive load**. The frequent feedback loops provided by AI assistants act as constant interruptions, preventing engineers from entering the deep, focused states required for mastery. Furthermore, as tasks shift from creation to verification, the work begins to feel less like a craft and more like a clerical duty. Vella warned leaders that measuring productivity alone is insufficient; focusing strictly on output while ignoring the erosion of the craft will inevitably lead to **AI burnout**. Reclaiming the Craft through Augmentation Despite the risks of decay, both Howard and Vella emphasized that AI can be a powerful tool for **augmenting human intellect** rather than replacing it. This vision aligns with the historical missions of computing pioneers like Douglas Engelbart, who sought to organize intellectual capabilities into higher levels of synergistic structuring, and Kenneth Iverson, who viewed notation as a tool of thought. To achieve this, developers must intentionally use AI to support growth—using it to learn new foundational principles, experiment under demand, and tackle more complex tasks than they could alone. Howard demonstrated this approach through a tool called Solveit, designed to augment understanding. Rather than asking the AI to "do the work," the interaction focuses on clarifying complex papers, generating concrete examples for abstract concepts, and assisting in effortful debugging. The goal is to keep the human in the loop of craft, ensuring that every line of code or architectural decision is understood and verified by the person responsible. This requires a shift in mindset: moving away from the "done for you" marketing of AI platforms and toward a self-determined pursuit of mastery. Sovereignty and the Mesh Alternative to Hyperscalers While the psychological aspect of AI is critical, Mike Neil of Block addressed the physical constraints and power dynamics of the industry. Currently, AI development is heavily dependent on a few **hyperscalers** who own the massive compute resources required for large-scale models. This creates a crisis of **sovereignty**, where data and national interests are tied to fiber optic cables running to a handful of data centers in California. Neil pointed out the massive environmental impact of these centers, noting that the embodied energy in consumer devices—laptops and phones—often represents the majority of their lifetime energy consumption. Neil's proposed solution involves tapping into the vast, idle compute power already present in consumer devices. By creating a decentralized **mesh protocol**, we can move toward **zero marginal cost compute** and local model execution. This would not only reduce the climate impact of centralized data centers but also provide developers and nations with the optionality of where their models run. Sovereignty in the AI era isn't just about who writes the code, but who owns the hardware that executes it. Transitioning away from total dependence on hyperscalers is a necessary step for maintaining long-term autonomy in the software industry. Designing Your Own Future in the AI Era The future of software engineering likely splits into three paths: the **Artisanal Developer** (hand-coding for high-safety domains), the **Clerical Coder** (uncritically accepting AI PRs), and the **Orchestrator** (the code conductor building machines that build machines). The most significant predictor of success in this new landscape isn't seniority or company size, but **self-efficacy**—one's belief in their ability to accomplish tasks with these new tools. Unlike demographics, self-efficacy is a belief that can be changed through experimentation and mastery experiences. As the industry is "rebooted," every developer has become an explorer again. The challenge for both individual engineers and tech leaders is to design systems and workflows that prioritize **pride and joy** in the craft. We must resist the gravitational pull of "clerical coding" and dark flow, instead choosing pathways that encourage deep domain expertise and the construction of robust harnesses. By focusing on how work makes the person, rather than just the output the person makes, the software community can ensure that AI remains a tool for human flourishing rather than a source of professional diminishing.