Listen to this Post
Introduction:
The contemporary discourse on artificial intelligence often centers on job displacement, framing it as a battle between human and machine. However, a more precise and technical analysis reveals that AI is not “stealing” roles so much as it is automating the procedural layers of work. By treating repetitive, rule-based tasks as executable code, AI acts as a powerful interpreter for shallow processes, effectively exposing which professional functions are merely well-documented APIs and which require true kernel-level comprehension. This shift demands a fundamental re-evaluation of skill acquisition, moving from memorizing command syntax to understanding system architecture and intent.
Learning Objectives:
- Objective 1: Distinguish between procedural knowledge (executing steps) and conceptual knowledge (understanding the system) in the context of IT and cybersecurity.
- Objective 2: Analyze how AI tools function as “compilers” for routine tasks, and identify which professional activities are most susceptible to automation.
- Objective 3: Develop strategies to transition from a role focused on task execution to one centered on architectural design, threat modeling, and strategic oversight.
You Should Know:
- Deconstructing the “Paperwork Cosplay”: From GUI Clicks to CLI Automation
The post highlights “paperwork cosplay” and “slide-deck theater” as trivial work. In technical terms, this refers to tasks that are heavily standardized and lack unique problem-solving. Just as a system administrator moves from manually configuring a server via a GUI to automating it with a script, AI now automates these higher-level “procedures.”
Step‑by‑step guide: Identifying Automatable Tasks
- Audit Your Daily Commands: List the top 10 commands or sequences you run daily (e.g.,
grep,awk, `systemctl` restarts, log checks).
– Example: `sudo grep “Failed password” /var/log/auth.log | awk ‘{print $11}’ | sort | uniq -c`
2. Abstract the Logic: What is the intent? “Check for failed SSH login attempts and count the source IPs.” This is a rule-based procedure.
3. Compare with an AI If you were to ask an AI, “Show me the top 10 IPs trying to brute-force SSH on my Linux server,” it would generate a variation of the command above. The AI has automated the “how.”
4. Assess Your Value: Your value wasn’t in knowing `grep` and `awk` syntax (the procedure), but in knowing why you need to check for failed logins, what a brute-force attack looks like, and what action to take next (e.g., adding the IPs to an `iptables` blocklist or configuring fail2ban).
- The “Interface vs. Underlying Model”: A Tale of Two Firewalls
The author distinguishes between knowing the interface and knowing the underlying model. In cybersecurity, this is the difference between someone who can click through a cloud console to set up a firewall and someone who understands the underlying network protocols and access control lists (ACLs).
Step‑by‑step guide: Hardening a Cloud Firewall (AWS Security Group as an example)
1. The Interface Way (Procedural): Log in to AWS Console -> Navigate to EC2 -> Security Groups -> Click “Create Security Group” -> Add inbound rule for port 22 from 0.0.0.0/0 (allowing all SSH) -> Click “Save”.
– Risk: This creates a massive security vulnerability. The procedure was followed, but the model (network security principle of least privilege) was ignored.
2. The Model Way (Conceptual): Understand that a Security Group is a stateful firewall.
– Step 1: Identify the specific services running (e.g., a web server on port 443, an admin SSH on a non-standard port).
– Step 2: Determine the specific source IPs or ranges that need access (e.g., your office’s public IP for SSH, `0.0.0.0/0` for the public web server port 443).
– Step 3: Implement using Infrastructure as Code (IaC) like Terraform to enforce the model, not just the clicks.
resource "aws_security_group_rule" "allow_ssh_from_office" {
type = "ingress"
from_port = 22
to_port = 22
protocol = "tcp"
cidr_blocks = ["YOUR.OFFICE.IP/32"] The model: specific access
security_group_id = aws_security_group.web_sg.id
}
The AI can write the Terraform, but it needs a human with the model to define YOUR.OFFICE.IP/32.
3. “You Were the API”: Replacing Human Middleware
The post’s stark assertion, “you were the API,” frames the human as a connector between systems. In IT, this is the role of a junior analyst who receives a ticket (input), performs a set of manual checks (function), and outputs a report. This is being replaced by integrated API calls and automated workflows.
Step‑by‑step guide: Automating a Human API with a Python Script
Imagine a task: receive an IP, check it against VirusTotal, and log the result.
1. The Human API (Old Way): Analyst opens browser -> goes to virustotal.com -> enters IP -> copies result -> pastes into a log.
2. The Scripted API (New Way):
import requests
import time
def check_ip_virustotal(ip_address, api_key):
url = f"https://www.virustotal.com/api/v3/ip_addresses/{ip_address}"
headers = {"x-apikey": api_key}
response = requests.get(url, headers=headers)
if response.status_code == 200:
data = response.json()
malicious_count = data['data']['attributes']['last_analysis_stats']['malicious']
return f"IP {ip_address} flagged as malicious by {malicious_count} engines."
else:
return f"Error: {response.status_code}"
Example usage (replace with your API key)
api_key = "YOUR_VT_API_KEY"
ip_to_check = "8.8.8.8"
result = check_ip_virustotal(ip_to_check, api_key)
print(result)
with open("scan_log.txt", "a") as logfile:
logfile.write(f"{time.ctime()}: {result}\n")
The analyst’s role evolves from executing the API call to managing the API keys, handling errors, and interpreting the results within a broader threat intelligence context.
- AI’s 80% Ceiling: The Architecture in My Head
The post notes AI’s brilliance up to 80%, where it fails to track “higher-level intent.” In software development and security architecture, this is the gap between writing a function and designing a secure, scalable system. An AI can write a function to sanitize user input, but it may not grasp how that function fits into a defense-in-depth strategy against SQL injection or Cross-Site Scripting (XSS).
Step‑by‑step guide: From Code Snippet to Secure Architecture
- The AI Task (80%): “Write a Python function to sanitize user input for a database query.”
– AI Output: It might produce a function using escape_string().
2. The Architect’s Intent (The Remaining 20%):
- Step 1: Recognize that input sanitization is a flawed, last-line defense. The higher-level intent is to prevent injection entirely.
- Step 2: Implement the architectural solution: Use Parameterized Queries/Prepared Statements. This separates SQL logic from data, making injection impossible regardless of input content.
Secure architectural approach, not just sanitization import sqlite3 def get_user_by_id(user_id): The intent is to safely query the database conn = sqlite3.connect('example.db') cursor = conn.cursor() This is the architectural fix: the parameter is a placeholder, not part of the SQL string cursor.execute("SELECT FROM users WHERE id = ?", (user_id,)) return cursor.fetchall()The AI can write the code, but the architect understands the principle (defense in depth, parameterization) that dictates which code should be written.
- The Simulation of Expertise vs. Comprehension of Wisdom
The article argues AI can simulate expertise but lacks wisdom. In a security context, this is the difference between an AI that can identify a known malware hash (signature-based detection) and a human analyst who can detect a novel, zero-day attack based on anomalous behavioral patterns (heuristic/threat hunting).
Step‑by‑step guide: Basic Threat Hunting (Moving Beyond AI’s Simulation)
1. AI’s Simulation: “Alert: Known malicious hash `e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855` detected.”
2. Human’s Comprehension (The Hunt):
- Command (Linux): Use `auditd` to monitor for unusual process ancestry.
Search for a shell being spawned by a non-standard parent process (e.g., a browser) sudo ausearch -k process_audit | grep 'shell' | grep 'browser'
- Analysis: The command output shows `pid=1234 (browser)` spawning
pid=1235 (bash). This is highly suspicious. A known malware hash wasn’t found, but the behavior (a browser spawning a shell) indicates a potential compromise. The human analyst comprehends the context of the event, which the signature-based AI simulation would miss.
What Undercode Say:
- Key Takeaway 1: Your professional value is inversely proportional to how easily your tasks can be described as a sequence of if/then statements. The more your work resembles a manual API call, the more directly AI will automate it.
- Key Takeaway 2: The future belongs to those who understand “why” the system exists, not just “how” to navigate it. The security architect who grasps the principles of zero-trust will always be more valuable than the engineer who can only follow a checklist to implement it.
Analysis: José da Veiga’s commentary cuts through the hype to diagnose a structural vulnerability in the modern workforce: a systemic over-reliance on procedural knowledge at the expense of foundational understanding. AI is not a thief, but a mirror. It reflects the mechanistic, shallow nature of roles that were already ripe for optimization. The fear surrounding job displacement is, in this view, a symptom of a deeper rot—a professional identity built on being a “competent executor” rather than a “critical thinker.” The response to this shift cannot be to fight the automation, but to transcend it. In cybersecurity and IT, this means moving up the stack from managing tools to defining strategy, from responding to alerts to hunting for threats, and from following compliance checklists to designing resilient, secure systems. The machines are taking over the score; the only safe place left is composing the music.
Prediction:
Within the next three to five years, we will see the emergence of a pronounced “comprehension gap” in the technical workforce. Entry-level roles focused on routine execution (e.g., SOC Tier 1 analysts, junior cloud admins) will be heavily compressed or eliminated. This will create a barbell effect: a high demand for senior architects and strategists who can define intent and manage AI systems, and a hollowing out of mid-level roles. The most significant consequence for the security landscape will be an increase in “procedural attacks”—exploits that target the gaps in AI-generated code or configurations that lack the human architect’s overarching intent, forcing a new era of adversarial AI and human-in-the-loop validation.
▶️ Related Video (74% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Josedaveiga Yes – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
