🧠 AI and Bureaucracy: Disruptor or Enabler?

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Welcome back AI prodigies!

In today’s Sunday Special:

• 🏬Bureaucracies Suck

• 😑But We Need Them

• 🤖Can AI Overcome Inertia?

• 🔑Key Takeaway

Read Time: 5 minutes

🎓Key Terms

• Inductive Reasoning: a logical process that involves using specific experiences, observations, or facts to evaluate situations and draw general conclusions.

• Large Language Models (LLMs): AI models pre-trained on vast amounts of data to generate and classify text, conversationally answer questions, and translate languages.

🏬BUREAUCRACIES SUCK

For better or for worse, we’ll interact with hundreds of bureaucracies throughout our lifetimes. From university administrators to retail customer support to our employers, bureaucracies or groups of unelected or unaccountable administrators that make important decisions on behalf of an organization are everywhere. Many bureaucratic interactions are fraught with frustration, stemming from longer-than-expected wait times or redirection after redirection through a labyrinth of customer service departments. In recent years, bureaucracies in America have come under steady fire. Former presidential hopeful Vivek Ramaswamy famously promised to fire 75% of all federal employees in his first term. Though appeals to anti-authority sentiment resonate in the American electorate, bureaucracies are likely necessary to organize large groups of people to accomplish enormous tasks, despite their slow and wasteful tendencies.

😑BUT WE NEED THEM

Building valuable products, from Google Search to vaccines, requires leadership, task delegation, division of labor, and channels to move information up, down, and across the chain of command. For thousands of years, conventional wisdom was the following: the more complex a project, the larger the bureaucracy required to manage it. But is that a law of physics? To find out, let’s first learn the four critical features of bureaucracies, courtesy of Michael Barnett, author of Rules for the World: International Organizations in Global Politics:

1. Hierarchy: clearly defined spheres of competence and divisions of labor.

2. Continuity: a system where administrators have a full-time salary and advance within the structure.

3. Impersonality: prescribed rules and operating rules rather than arbitrary actions.

4. Expertise: officials are chosen according to merit, have been trained, and hold access to knowledge.

To some, one or more of these four features may seem meaningless. Power-hungry administrators overcomplicate everything to maintain their salary, status, and facade of service. To others, accomplishing anything significant will always require a delicate balance of hierarchy, continuity, impersonality, and expertise. Now enter AI—allegedly the most potent disruptive force in our lifetime.

🩺 PULSE CHECK

🤖CAN AI OVERCOME INERTIA

Time will tell. In the meantime, let’s explore the potential effects. AI copilots fail to disrupt hierarchies since they don’t eliminate the need for competence or task delegation. Sound intuition, inductive reasoning, and emotional intelligence are most valuable in a world where AI generates many solutions to any given problem. Coding copilots predict the following line of code or offer debugging solutions. Still, it’s up to you to align its recommendations with computational efficiency, other production requirements, or other classes in the codebase. Law copilots generate rationales for a particular verdict, but Large Language Models (LLMs) fail to reason independently, favoring probabilistic word prediction rather than seeing the big picture. And art generators face increasing backlash as AI art feels cheap and soulless, the product of spammers and scammers. AI is the rising tide that lifts all boats, but rafts, dinghies, catamarans, and yachts are not all cut from the same cloth. When every professional in every domain has a copilot and has taken the same prompt engineering course, competitive advantage comes from doing human stuff better than others.

AI-driven efficiency gains are nothing to scoff at. But do they improve the efficiency of the bureaucracy? On the net, yes. As productivity per worker increases, each function or level in the hierarchy may cut a significant portion of the headcount, resulting in higher productivity for the bureaucracy. However, AI copilots cannot eliminate layers of bureaucracy. As AI deployment increases, organizations need more machine-learning practitioners to deploy and fine-tune models, cybersecurity and legal personnel to mitigate risks like data leaks and copyright infringement, and data scientists to prepare data for model training. These needs will only multiply if organizations elect to build custom LLMs.

🔑KEY TAKEAWAY

In Barnett’s terms, experts enjoy continuity in the impersonality of an organizational hierarchy. In other words, individuals climb the ranks by being competent and adhering to both written and unwritten rules. To the public’s dismay, AI may exacerbate the unintended consequences of bureaucratic bloat—misaligned incentives, workplace politics, and painfully lethargic execution. Individual productivity gains may fail to translate to organizational productivity, especially if new AI-related rules and roles fail to keep up with the pace of innovation.

📒FINAL NOTE

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