🧠 How AI Creates an Illusion of Efficiency

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Welcome back AI prodigies!

In today’s Sunday Special:

• 📜The Prelude

• 💥How Do We Make Decisions?

• 💭The New Cognitive Bottleneck

• ⚖️The True Cost of Efficiency

• 🔑Key Takeaway

Read Time: 7 minutes

🎓Key Terms

• Generative AI (GenAI): Creates entirely new content that replicates human-like creativity.

• Rebound Effect: When daily tasks become easier, we often do more of them, saving less time overall.

🩺 PULSE CHECK

📜THE PRELUDE

While composing an email in Gmail, you might notice interactive sentence completion suggestions as you type. Since 2018, Smart Compose has saved precious seconds on every email. So, how does it work?!

Smart Compose looks at the words you’ve already typed and predicts the most likely next word, phrase, or statement. It’s limited to short, context-specific continuations within predefined parameters. For example, if you begin typing “Looking forw....” it might suggest “Looking forward to hearing from you.”

Unlike Smart Compose, GenAI doesn’t just complete sentences; it creates them. GenAI can produce entirely new content on virtually any topic imaginable, with the potential to transform daily workflows by offering faster search, faster drafts, and faster analysis.

It’s no wonder we love GenAI. We’re impatient by nature, always seeking shortcuts to get what we want. But our love affair with efficiency and convenience may have unintended consequences.

So, how do we make decisions? How does GenAI shape how we form decisions? Why does using GenAI end up costing us more time down the line?

💥HOW DO WE MAKE DECISIONS?

⦿ 1️⃣ Cognition 101.

The process of making decisions has always been a balancing act of limits. In 1957, American scholar Herbert A. Simon observed that humans exhibit Bounded Rationality: people can never make the perfect decision because they always face constraints. Imagine choosing a snack at the store. You face three main constraints: 1. how much money you have, 2. how hungry you are, and 3. what’s available on the shelves. So, instead of searching endlessly for the perfect snack, you select the most suitable option within the given constraints.

Every decision requires four steps: 1. acquiring information, 2. storing it, 3. manipulating it, and 4. retrieving it.

The more complex the decision, the heavier the cognitive burden at each step. For instance, physicians must apply a slew of medical knowledge to patient histories, novel symptoms, and constantly changing clinical datasets. In surgical settings, clinicians follow the Surgical Safety Checklist before every patient procedure. This frees up cognitive resources to refine surgical approaches based on patient preferences.

⦿ 2️⃣ Technology and Cognition.

Technology has always chipped away at how humans make decisions. In the 1970s, calculators began to replace mental arithmetic. By the late 1990s, search engines stripped away the friction of finding relevant websites. In each case, technology automated the process of finding answers for specific, simple daily tasks.

When we prompt GenAI, it automates the four steps required in every simple daily decision: 1. acquiring information related to your prompt, 2. storing relevant context from your prompt, 3. pulling learned knowledge from training datasets, and 4. generating a response to your prompt. But because it lacks real-world understanding and requires human oversight, GenAI currently only replaces lower-order cognition: routine, generic, pattern-based thinking. For example, drafting emails or generating code snippets.

GenAI reshapes cognitive constraints. By automating the four steps required in making a simple daily decision, it introduces a crucial new fifth step: 5. interpretation. After we receive a response to our prompt, we must decide whether to ignore it, tweak it, or adopt it. If we can’t carefully decide how to use GenAI’s outputs, then all the time, energy, and effort saved using the technology could be wasted. In other words, if we don’t interpret GenAI’s outputs effectively, we lose the advantage of using it in the first place.

💭THE NEW COGNITIVE BOTTLENECK

⦿ 3️⃣ Amdahl’s Law.

In 1967, American computer architect Gene Amdahl developed a formula known as Amdahl’s Law: if you improve the efficiency of a single component within a system, performance gains are limited by its role in that system. In other words, when you speed up one part of a process, whatever you can’t speed up becomes the new choke point.

Consider the airport check-in process. You can install high-speed check-in kiosks with automated bag drops, but if only a few TSA Officers are available to check IDs, lines will form at that stage. The slowest step dictates the pace of the system.

GenAI transforms daily digital workflows by making drafting, synthesizing, and planning dramatically faster. Yet, a new bottleneck emerges as the crucial new fifth step of interpreting GenAI’s outputs becomes the prevailing pace-setter.

⦿ 4️⃣ Case Study: Paralegal Workflows.

Few professions illustrate the shifting bottlenecks of GenAI more clearly than those of paralegals. Traditionally, much of their work involved lower-order cognition: preparing discovery documents, summarizing case law, and organizing exhibits. These tasks were time-consuming but relatively standardized, making them ideal candidates for automation.

GenAI platforms like Harvey can synthesize testimonies, summarize depositions, and generate discovery requests in minutes. What once took a paralegal hours can be spun up instantly, at zero marginal cost. From the outside, this appears to be a pure productivity win.

But the bottleneck simply shifts. Every AI-generated text must still be edited by a paralegal and later reviewed by an attorney. The professional obligations of law, such as accuracy, confidentiality, and legal liability, have only intensified. If GenAI introduces a subtle error, the responsibility lies with the law firm, not GenAI.

GenAI may not increase efficiency when it amplifies existing constraints. In this case, the ability to generate legal documents in minutes results in more content for paralegals and attorneys to review for completeness, consistency, and basic legal accuracy. As a result, the bottleneck simply shifts from drafting to reviewing, offsetting the efficiency gains GenAI was meant to deliver. This phenomenon is known as the Rebound Effect.

⚖️THE TRUE COST OF EFFICIENCY

⦿ 5️⃣ The Rebound Effect.

In 1865, English economist William Stanley Jevons noticed something surprising. When engineers invented more efficient engines that used less coal, Britain’s total coal consumption actually increased. Why?! Because the more efficient engines made industrial processes cheaper to operate. As a result, more industries adopted them, and coal consumption skyrocketed.

We see a similar phenomenon in human behavior today. People who drive fuel-efficient vehicles often drive farther than they otherwise would because they feel like they’re “getting more per gallon.” This mindset can reduce the expected fuel savings from technological advances in transportation by as much as 30%.

The same phenomenon happens when we use GenAI. When it reduces the mental effort required to complete a simple daily task, we don’t just finish that simple daily task faster; we end up doing more of them. What appears to initially save time can actually create additional work later on. For instance, GenAI might generate initial drafts of legal documents in minutes, but reviewing the increased volume of initial drafts for legal accuracy can take as much time or even more time than before using GenAI.

🔑KEY TAKEAWAY

GenAI can automate almost every simple daily digital task we need to do. While it promises and often delivers productivity gains, the burden of judging, refining, and applying GenAI’s outputs still rests firmly on our shoulders. Oftentimes, this burden is so great that we’d be better off doing the simple daily digital task ourselves. In a world where first drafts are free, we must remember that shortcuts are often short-term.

📒FINAL NOTE

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