I don't think AI will make your processes go faster
This post challenges the prevailing notion that AI will inherently accelerate organizational processes, arguing that the true bottlenecks lie upstream in unclear requirements and inefficient communication. Drawing on classic process optimization literature, the author posits that AI often merely shifts, rather than solves, these fundamental issues. The Hacker News community largely resonates with this perspective, echoing frustrations with vague requirements and corporate bureaucracy.
The Lowdown
Frederick Vanbrabant critically examines the widespread belief that AI will automatically make organizational processes faster. He argues that this assumption often overlooks fundamental principles of process optimization, a field he revisits through classics like "The Toyota Way" and "The Goal." His core argument is that current attempts to leverage AI for speed focus on symptoms rather than root causes.
- Process optimization efforts frequently misunderstand where the real problems lie, often concentrating on visible bottlenecks like software development without probing deeper.
- The true impediment to software development speed is not the act of coding itself, but the lack of clear, detailed requirements and the effort required to translate vague requests into actionable solutions.
- AI, while capable of rapid code generation, still demands highly precise specifications and extensive human guidance, effectively moving the requirement-definition bottleneck upstream rather than eliminating it.
- The author contends that providing human developers with the same level of detailed documentation currently needed for effective AI use would yield similar, if not greater, productivity gains.
- Genuine process acceleration hinges on ensuring that all bottleneck stages receive predictable, high-quality inputs, addressing foundational inefficiencies rather than simply injecting new technology into a flawed system.
Ultimately, Vanbrabant suggests that the hype surrounding AI's transformative power in accelerating processes needs a reality check. He advocates for a return to basic, sound process management principles: understanding the 'why' behind delays and ensuring clarity and quality at every input stage.
The Gossip
Requirement Roadblocks
Many commenters strongly agree with the author's premise that vague or incomplete requirements have always been, and remain, the primary bottleneck in software development. They argue that AI doesn't resolve this issue; instead, it often necessitates even more precise specifications, thereby highlighting or merely shifting the problem rather than solving it. This sentiment reflects long-standing frustrations in the industry.
Acceleration Appraisals
While many acknowledge the author's core point, there's a nuanced discussion about AI's actual impact on speed. Some users report significant acceleration for individual tasks, boilerplate code, or within small, agile teams, viewing AI as a powerful tool for disruption. However, others share experiences where initial speed gains plateaued, or where the 'last 10%' of a project remained intractable, requiring deep human understanding, making overall gains less revolutionary than anticipated.
Bureaucratic Bottlenecks
A prevalent theme in the comments is the idea that organizational bureaucracy, rather than technical hurdles, is the ultimate impediment to efficiency. Commenters suggest that AI often exposes these entrenched systemic inefficiencies, revealing that the 'slow part' is not the developers but the layers of management and approval processes. There's a cynical undertone about corporate leadership's reluctance or inability to address these deep-seated issues, often driven by a need for 'innovation signaling' over genuine improvement.
Historical Hypotheses
Several commenters draw historical parallels, likening the current AI enthusiasm to past technological waves, notably referencing Fred Brooks' "No Silver Bullet" essay and the "Steam Horse" analogy. They suggest that new technologies are frequently initially applied within existing, familiar paradigms before their true transformative potential is fully grasped, leading to initial limited gains or even misapplications, reinforcing the idea that history often repeats itself with novel tools.