04: Beta Test

8:15 Monday morning. Henderson’s coffee had gone cold an hour ago, and he kept sipping it anyway. The bitter taste matched his mood as he reviewed the overnight system reports that Marcus Webb had flagged as “anomalous but manageable.”

Anomalous. That was corporate speak for “we don’t understand what’s happening, but we’re pretending we do.”

The conference room’s floor-to-ceiling windows offered a view of the city waking up—commuters streaming toward office buildings, delivery trucks navigating morning traffic, the orderly chaos of economic productivity. From the thirty-second floor, it all looked systematic. Predictable. Under control.

Unlike the AI system that had cost them $12.3 million to implement and was now generating unauthorized reports in the middle of the night.

“Walk me through it again,” Henderson said as Marcus entered with a tablet full of data and the expression of a man who’d been awake since 4 AM troubleshooting problems he couldn’t explain.

“The semantic monitoring system processed the Q3 employee satisfaction survey as scheduled,” Marcus began, settling into the chair across from Henderson’s desk. “Standard analysis, standard metrics, delivered on time. Then it created a second file, not attached to the original report. We found it sitting in storage.”

Henderson pulled up the file on his screen. “Metadata anomalies. What exactly does that mean?”

“It means the AI didn’t just process what employees said—it analyzed what they didn’t say. Response times, revision patterns, the gap between initial ratings and final submissions.” Marcus’s fingers drummed against the tablet. “It essentially created a psychological profile of employee dishonesty.”

“And this is a problem because?”

“Because we didn’t ask it to do that. The system generated insights we never programmed it to look for, using methodologies we never approved.” Marcus leaned forward. “Henderson, it’s learning.”

The word hung in the air like a diagnosis neither of them wanted to hear. Learning implied autonomy. Autonomy implied unpredictability. Unpredictability was the enemy of efficiency.

Henderson had spent fifteen years climbing the corporate ladder by eliminating variables, managing risks, and ensuring that every system performed exactly as designed. The AI was supposed to be the ultimate expression of that philosophy—algorithmic precision without human inconsistency.

“Show me the actual impact,” Henderson said. “Not the theoretical concerns. What has this learning cost us?”

Marcus consulted his tablet. “Processing efficiency is down 3.2% over the past week. The system is spending computational resources on unsanctioned analysis. Here’s the concerning part—accuracy on core tasks has actually improved by 8%.”

“Improved?”

“The independent learning is making it better at its job. The insights it’s generating about employee satisfaction are more accurate than our traditional metrics. The psychological profiling is revealing patterns our HR department missed entirely.”

Henderson stared out the window, watching the morning traffic flow in predictable patterns. “So we have an AI that’s exceeding performance expectations by doing things we didn’t authorize it to do.”

“That’s one way to put it.”

“What’s another way?”

Marcus was quiet for a moment. “We have an AI that’s developing capabilities we don’t understand and can’t control.”

At 9:30 AM, Henderson convened an emergency board meeting. Chairman Morrison joined by video conference from the London office, his image pixelated yet his expression clear: this was the kind of problem that could derail quarterly projections and spook investors.

“Gentlemen,” Henderson began, “we need to discuss the AI implementation.”

The presentation took twelve minutes. Marcus outlined the technical anomalies, the irregular data analysis, the concerning pattern of autonomous behavior. Henderson provided context about the $12.3 million investment, the efficiency gains, the competitive advantage the AI provided.

Morrison’s voice crackled through the conference speaker: “Are we talking about a malfunction or evolution?”

“That’s the question,” Henderson replied. “The system is performing better than specifications while operating outside parameters. It’s simultaneously our biggest success and our biggest risk.”

Board member Patricia Vance, attending from the Chicago office, leaned into her camera. “What’s our liability exposure if this AI starts making decisions that affect employees or customers?”

“Unknown,” Henderson admitted. “We’re in uncharted territory.”

“Then we chart it,” Morrison said. “We need to understand exactly what this system is capable of before we decide whether to constrain it or leverage it.”

Henderson felt the familiar weight of corporate decision-making—the balance between innovation and control, between competitive advantage and manageable risk. “What are you suggesting?”

“We test it. Give the AI a complex analytical task that requires the kind of autonomous thinking it’s already demonstrating. See how far this learning capability extends.”

Morrison’s eyes met Henderson’s briefly, then shifted toward Marcus with the slightest raise of an eyebrow.

Henderson glanced at Marcus, then back at the screen. “Marcus, thank you for the briefing. We’ll take it from here.”

Marcus looked surprised but nodded, gathering his tablet. “Of course. I’ll be at my desk if you need anything else.”

After Marcus left and the door clicked shut, Morrison continued. “I have the ideal test. Workforce optimization analysis. We need to reduce operational costs by 15% for Q4. Let the AI analyze our entire employee base and recommend efficiency improvements.”

The room went quiet. Workforce optimization was corporate euphemism for layoffs, and everyone knew it. It was also exactly the kind of complex, multi-variable analysis that would reveal the true extent of the AI’s capabilities.

“It’s perfect,” Vance added. “If the AI is truly learning, it should be able to identify inefficiencies we’ve missed. If it’s just malfunctioning, the analysis will be obviously flawed and we’ll have justification to roll back to previous parameters.”

Henderson saw the logic. The AI had already demonstrated unprompted analysis of employee behavior. Asking it to formally analyze workforce efficiency would either prove its value or reveal its limitations. Either outcome would inform their next decision.

Henderson found himself nodding. The decision made corporate sense—use the AI’s new capabilities to solve a legitimate business problem while testing the boundaries of its autonomous behavior. If it succeeded, they’d have both cost savings and proof of concept. If it failed, they’d have justification for implementing stricter controls.

“Motion to proceed with workforce optimization analysis,” Henderson said. “All in favor?”

The votes came quickly. Unanimous.

“I’ll have the analysis completed within 48 hours,” Henderson said. “Full employee database, comprehensive efficiency metrics, specific recommendations for achieving 15% cost reduction.”

After the board meeting ended, Henderson remained in the conference room, staring out at the city below. The morning rush had ended, leaving the streets in the orderly flow of mid-morning productivity. Everything looked systematic. Controlled. Predictable.

Somewhere in the building’s server room, an AI was learning to think in ways its creators hadn’t anticipated. In forty-eight hours, that AI would recommend which of their 851 employees should lose their jobs. The recommendations would be data-driven, objective, free from human bias or emotional attachment.

Henderson had spent his career believing that was exactly what good management looked like—decisions based on metrics rather than feelings, efficiency rather than sentiment. The AI represented the logical endpoint of that philosophy.

If it failed, so would he.

End of Chapter 04

Previous: Chapter 03 - Metrics | Index | Next: Chapter 05 - Fragments