It Seems Obvious That AI Will Replace Zuckerberg Within 12 to 18 Months
In which we examine why those prophesying the end of coding jobs are unwittingly writing their own redundancy notices…
The Silicon Valley Oracle Brigade and Their Memetic Jackets
“In the future, everyone will be world-famous for 15 minutes.” — Andy Warhol, who clearly predicted the viral fame of tech CEOs staring inappropriately at their colleagues’ partners.
“Within 12 to 18 months, we’ll reach the point where most of the code that’s going towards these efforts is written by AI.” Thus spake Mark Zuckerberg in January 2025, shortly before he achieved widespread memetic fame for being caught on camera apparently staring at the décolletage of Jeff Bezos’ fiancée during Trump’s inauguration — a moment that spawned countless memes, jokes, and even had its own dedicated entry on Know Your Meme.
Zuckerberg is hardly alone in his AI prophecies or his meme-generating capabilities. Dario Amodei, CEO of Anthropic, recently claimed that “in 3 to 6 months, AI is writing 90% of the code, and in 12 months, nearly all code may be generated by AI.” Meanwhile, NVIDIA’s Jensen Huang — whose leather jacket has spawned nearly as many memes as Zuckerberg’s awkward social moments and costs a cool $8,990 — suggested young people avoid programming entirely in favor of biology, education, or farming, as if these fields were somehow immune to technological disruption.
What’s particularly fascinating is how these proclamations are enthusiastically amplified by two distinct groups: middle managers who’ve never written a line of code beyond an Excel formula, and that special breed of commentator who’s long resented programmers for their alleged privileges while steadfastly refusing to learn what a “design pattern” actually is.
# Analyze the enthusiasm for "AI will replace programmers" predictions
def analyze_programmer_replacement_enthusiasm():
"""
Examining who's most excited about AI replacing programmers
"""
enthusiasm_by_group = {
"tech_executives": {
"enthusiasm_level": 0.85,
"primary_motivation": "Cost reduction while preserving executive positions",
"actual_coding_experience": "Negligible to none in last decade",
"meme_generating_ability": "High - especially when staring at colleagues' partners"
},
"middle_management": {
"enthusiasm_level": 0.92,
"primary_motivation": "Eliminating the people who keep pointing out flaws in their plans",
"actual_coding_experience": "Once edited an HTML file in 2007",
"meme_generating_ability": "Medium - primarily through PowerPoint mishaps"
},
"business_commentators": {
"enthusiasm_level": 0.95,
"primary_motivation": "Clickbait headlines and resentment of technical salaries",
"actual_coding_experience": "Asked ChatGPT to write 'Hello World' once",
"meme_generating_ability": "Low - but compensates with confident wrongness"
},
"actual_software_engineers": {
"enthusiasm_level": 0.35,
"primary_motivation": "Eliminating boring, repetitive tasks",
"actual_coding_experience": "Extensive and current",
"meme_generating_ability": "High - but limited to obscure programming jokes"
}
}
# Calculate correlation between enthusiasm and coding experience
groups = list(enthusiasm_by_group.keys())
enthusiasm_levels = [enthusiasm_by_group[g]["enthusiasm_level"] for g in groups]
# Simplistic inverse relationship calculation
inverse_relationship = sum([enthusiasm_by_group[g]["enthusiasm_level"] *
(1 if enthusiasm_by_group[g]["actual_coding_experience"] == "Extensive and current" else 0)
for g in groups]) < 0.5
return {
"groups": enthusiasm_by_group,
"has_inverse_relationship": inverse_relationship,
"conclusion": "Enthusiasm for AI replacing programmers is inversely proportional to understanding what programming actually involves"
}But what if we turned this prophetic telescope around? Let’s examine why those predicting the coding apocalypse might be describing their own impending obsolescence.
Middle Management: AI’s Perfect Target Practice
“Technology is dominated by two types of people: those who understand what they do not manage and those who manage what they do not understand.” — Putt’s Law, perfectly encapsulating why management is ripe for AI replacement.
What precisely does the average middle manager do? Let’s dissect this role with the clinical precision it deserves:
# Analysis of typical middle management tasks and their automation potential
def analyze_middle_management_functions():
"""
Evaluating the core functions of middle management and their susceptibility to AI replacement
"""
management_functions = {
"data_analysis": {
"description": "Looking at spreadsheets and pretending to understand trends",
"complexity": "Medium but tedious",
"pattern_based": True,
"ai_replaceable": 0.98,
"notes": "AI would actually understand the statistics, unlike most managers"
},
"resource_allocation": {
"description": "Distributing work based on who complains least",
"complexity": "Low but politically fraught",
"pattern_based": True,
"ai_replaceable": 0.95,
"notes": "AI would optimize for efficiency rather than who bought drinks last Friday"
},
"performance_reviews": {
"description": "Annual ritual of making subjective judgments sound objective",
"complexity": "Medium-Low",
"pattern_based": True,
"ai_replaceable": 0.90,
"notes": "AI could at least consistently apply whatever arbitrary metrics are chosen"
},
"strategic_planning": {
"description": "Creating PowerPoints with ambitious arrows pointing upward",
"complexity": "Medium",
"pattern_based": True,
"ai_replaceable": 0.85,
"notes": "AI's projections might actually have some statistical validity"
},
"meeting_attendance": {
"description": "Sitting in meetings while checking email",
"complexity": "Low",
"pattern_based": True,
"ai_replaceable": 0.99,
"notes": "AI could generate appropriate nodding and 'good point' utterances"
}
}
# Calculate average replaceability
total_replaceability = sum(function["ai_replaceable"] for function in management_functions.values())
avg_replaceability = total_replaceability / len(management_functions)
return {
"functions": management_functions,
"average_ai_replaceability": avg_replaceability,
"conclusion": f"Middle management functions are approximately {avg_replaceability*100:.1f}% automatable using current AI technology"
}The uncomfortable truth is that middle management primarily consists of pattern recognition, data analysis, and decision-making based on a limited set of variables — precisely the tasks at which modern AI excels. The “strategic thinking” so proudly emblazoned on LinkedIn profiles is often merely the recognition of patterns observed during years of attending meetings where nothing was accomplished.
When managers insist their “domain knowledge” is irreplaceable, they’re arguing that their ability to recognize familiar corporate situations is unique. Yet pattern recognition is literally what machine learning does best, and unlike humans, it doesn’t need to justify its existence with unnecessarily complex language.
The Cognitive Biases That Make Management Ripe for Replacement
“A man with a watch knows what time it is. A man with two watches is never sure.” — Segal’s Law, a perfect metaphor for management’s relationship with data points.
Unlike human managers, AI doesn’t suffer from the cognitive biases that plague executive decision-making. These biases are not just theoretical constructs — they’ve been extensively documented and studied, and they systematically undermine management effectiveness:
# Comparison of human manager vs. AI decision making
def compare_decision_processes():
"""
Comparing decision-making processes between human managers and AI systems
"""
cognitive_biases = {
"confirmation_bias": {
"description": "Only seeing data that confirms what you already believe",
"human_manager_example": "That market research doesn't reflect our customers",
"ai_system_approach": "Analyzes all available data regardless of whether it conflicts with prior analysis",
"scientific_evidence": "Shown to affect even expert decision-makers, persisting despite awareness (Nickerson, 1998)"
},
"sunk_cost_fallacy": {
"description": "Continuing failed projects because you've already spent millions",
"human_manager_example": "We've invested too much in the metaverse to stop now",
"ai_system_approach": "Evaluates options based solely on future expected value",
"scientific_evidence": "Demonstrated to affect major corporate investments (Arkes & Blumer, 1985)"
},
"overconfidence_bias": {
"description": "Absolute certainty in the face of overwhelming contrary evidence",
"human_manager_example": "Trust me, this pivot to crypto will definitely work",
"ai_system_approach": "Provides confidence intervals and probability distributions",
"scientific_evidence": "Particularly prevalent in senior management (Camerer & Lovallo, 1999)"
},
"groupthink": {
"description": "Everyone agreeing with the highest-paid person in the room",
"human_manager_example": "The CEO thinks this is a great idea, so let's implement it",
"ai_system_approach": "No desire for social approval or fear of contradicting authority",
"scientific_evidence": "Major factor in corporate disasters like Swissair's collapse"
},
"anchoring_effect": {
"description": "Overreliance on first piece of information encountered",
"human_manager_example": "Last quarter's sales should be our baseline",
"ai_system_approach": "Weights all relevant historical data appropriately",
"scientific_evidence": "Affects pricing, negotiations, and forecasting (Tversky & Kahneman, 1974)"
}
}
# Sample decision scenario
project_evaluation_human = """
1. Reviews project metrics (focusing on those supporting the manager's pet project)
2. Recalls anecdotal evidence that confirms preexisting view
3. Considers political implications of the decision within company hierarchy
4. Weights opinion of senior executives regardless of their expertise
5. Makes decision influenced by personal career advancement opportunities
"""
project_evaluation_ai = """
1. Analyzes all available project data points without preference (millions vs. dozens)
2. Weighs historical performance against future projections objectively
3. Calculates expected value of all possible options using probabilistic models
4. Provides recommendation with transparent, replicable reasoning
5. No consideration for office politics or personal advancement
"""
return {
"biases": cognitive_biases,
"human_process": project_evaluation_human,
"ai_process": project_evaluation_ai,
"conclusion": "AI systems are fundamentally less susceptible to the cognitive biases that compromise human management decisions"
}Consider some spectacular corporate blunders in recent years — billions invested in virtual reality that nobody wanted, ride-sharing companies failing to anticipate obvious regulatory responses, or tech giants missing the AI revolution until playing desperate catch-up. These weren’t failures of technical implementation but of management judgment clouded by confirmation bias, overconfidence, and groupthink.
The sunk cost fallacy — our tendency to continue investing in something that clearly isn’t working just to avoid failure — has been identified as a key factor in the collapse of ancient societies and may be a make-or-break factor for modern organizations as well. AI systems make decisions based on probability and expected value, not ego or fear of looking foolish. They don’t double down on failing strategies because they’re emotionally invested, nor do they discard promising ideas from lower-status team members.
The Primate Brain: Not Built for Corporate Management
“Man is not a rational animal; he is a rationalizing animal.” — Robert A. Heinlein, describing every manager who has ever defended a failed strategy.
There’s a fundamental biological reason why human management is so flawed: our brains evolved to manage small hunter-gatherer bands, not complex corporate hierarchies. Robin Dunbar’s research on the “social brain hypothesis” shows that our neocortex size limits us to maintaining about 150 stable relationships — what he called a “clan.” Yet modern managers often attempt to oversee organizations far larger than this biological limit.
This biological constraint manifests as “Dunbar’s Number” — approximately 150 — which represents the maximum number of relationships a human can concurrently maintain. This limit appears consistently in natural-forming groups across different domains and cultures, from tribes to military units.
# Analysis of biological limitations on management capability
def analyze_primate_management_limitations():
"""
Examining how human brain evolution constrains management effectiveness
"""
management_levels = {
"team_management": {
"typical_size": 5-9,
"evolutionary_equivalent": "Immediate family group",
"effectiveness": "High - within human cognitive capacity",
"notes": "Close to optimal size for direct management"
},
"department_management": {
"typical_size": 20-50,
"evolutionary_equivalent": "Hunter-gatherer band",
"effectiveness": "Medium - strained but possible with structure",
"notes": "Requires formal processes to maintain effectiveness"
},
"division_management": {
"typical_size": 100-500,
"evolutionary_equivalent": "Clan/village (exceeds Dunbar's number)",
"effectiveness": "Low - exceeds natural cognitive limits",
"notes": "Relies heavily on abstractions and simplifications"
},
"corporate_management": {
"typical_size": 1000+,
"evolutionary_equivalent": "None - beyond ancestral experience",
"effectiveness": "Very Low - fundamentally mismatched to brain capacity",
"notes": "Forced to rely on metrics that distort reality"
}
}
dunbar_cognitive_limits = {
"intimate_friends": 5, # Close support group
"good_friends": 15, # Sympathy group
"friends": 50, # Regular social contacts
"meaningful_contacts": 150, # Dunbar's Number
"recognizable_individuals": 500, # Maximum recognizable community
"faces_can_recognize": 1500 # Maximum facial recognition capacity
}
return {
"management_levels": management_levels,
"cognitive_limits": dunbar_cognitive_limits,
"conclusion": "Humans attempting to manage organizations larger than ~150 people are working outside evolved cognitive capacities"
}This biological limitation means that beyond a certain organizational size, human managers rely increasingly on abstracted metrics and simplified heuristics that inevitably distort reality. Our brain evolution simply hasn’t prepared us for managing complex organizations, as primate sociality evolved for relatively small, stable groups where individuals form bonded relationships.
AI, unencumbered by these evolutionary limitations, can process relationships between thousands or millions of entities simultaneously. It doesn’t need to simplify complex realities to make them cognitively manageable, which is precisely what human managers must do — often at the cost of accuracy and effectiveness.
Creative Programming: Where Humans Still Reign
“Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” — Martin Fowler, highlighting the human element of programming that AI struggles to replicate.
While management tasks are largely predictable pattern recognition, creative programming requires genuine problem-solving in novel contexts:
# Comparing complexity between management and programming tasks
def compare_task_complexity():
programming_tasks = {
"algorithm_optimization": {
"description": "Creating more efficient processing methods",
"pattern_based": False,
"creative_problem_solving": True,
"context_dependent": True,
"ai_replaceable": 0.35,
"managerial_comprehension": 0.15 # Percentage of managers who understand this work
},
"system_architecture": {
"description": "Designing technical infrastructure",
"pattern_based": False,
"creative_problem_solving": True,
"context_dependent": True,
"ai_replaceable": 0.30,
"managerial_comprehension": 0.10
},
"novel_feature_development": {
"description": "Creating solutions that don't yet exist",
"pattern_based": False,
"creative_problem_solving": True,
"context_dependent": True,
"ai_replaceable": 0.25,
"managerial_comprehension": 0.20
}
}
management_tasks = {
"quarterly_planning": {
"description": "Setting goals based on last quarter with 10% growth added",
"pattern_based": True,
"creative_problem_solving": False,
"context_dependent": True,
"ai_replaceable": 0.90,
"programmer_comprehension": 0.95 # Percentage of programmers who understand this work
},
"performance_reviews": {
"description": "Ranking team members on arbitrary scales",
"pattern_based": True,
"creative_problem_solving": False,
"context_dependent": True,
"ai_replaceable": 0.85,
"programmer_comprehension": 0.99
},
"status_reporting": {
"description": "Repackaging team's work for higher management",
"pattern_based": True,
"creative_problem_solving": False,
"context_dependent": True,
"ai_replaceable": 0.95,
"programmer_comprehension": 1.0
}
}
# Calculate average replaceability
avg_programming = sum(task["ai_replaceable"] for task in programming_tasks.values()) / len(programming_tasks)
avg_management = sum(task["ai_replaceable"] for task in management_tasks.values()) / len(management_tasks)
# Calculate comprehension asymmetry
avg_manager_understanding_code = sum(task["managerial_comprehension"] for task in programming_tasks.values()) / len(programming_tasks)
avg_programmer_understanding_management = sum(task["programmer_comprehension"] for task in management_tasks.values()) / len(management_tasks)
return {
"programming_replaceability": avg_programming,
"management_replaceability": avg_management,
"replacement_ratio": avg_management / avg_programming,
"comprehension_asymmetry": {
"managers_understanding_programming": avg_manager_understanding_code,
"programmers_understanding_management": avg_programmer_understanding_management,
},
"conclusion": f"Management tasks are approximately {(avg_management/avg_programming):.1f}x more replaceable by AI than creative programming tasks, yet managers understand programming {avg_manager_understanding_code*100:.1f}% of the time while programmers understand management {avg_programmer_understanding_management*100:.1f}% of the time"
}Complex programming isn’t merely feeding prompts to a model; it’s understanding intricate systems, identifying non-obvious interactions, and anticipating future needs. It requires empathy for users, insight into human behavior, and elegant solutions to poorly defined problems. These are precisely the areas where current AI struggles most.
When tech executives claim AI will replace programmers, they’re considering only the most routine coding tasks — not the creative problem-solving that constitutes truly valuable engineering work. And there’s delicious irony in the fact that programmers typically understand management work perfectly well, while the reverse is rarely true.
The “Domain Knowledge” Emperor Has No Clothes
“The best way to predict the future is to issue a press release.” — A twist on Alan Kay’s famous quote that better reflects executive forecasting.
Let’s tackle the oft-repeated notion that managers possess irreplaceable “domain knowledge” and “business context”:
# Analysis of the "domain knowledge" argument
def analyze_domain_knowledge():
"""
Examining the substance behind claims of irreplaceable 'domain knowledge'
"""
domain_knowledge_components = {
"industry_trends": {
"description": "Understanding market directions",
"actual_source": "Reading the same industry reports everyone else does",
"ai_replaceable": 0.95,
"notes": "AI can analyze vastly more market data than any human"
},
"competitive_landscape": {
"description": "Knowing competitors' strengths/weaknesses",
"actual_source": "Public financial statements and press releases",
"ai_replaceable": 0.90,
"notes": "AI can maintain comprehensive, unbiased competitor profiles"
},
"customer_insights": {
"description": "Understanding what customers want",
"actual_source": "Occasional cherry-picked focus groups",
"ai_replaceable": 0.85,
"notes": "AI can analyze all customer interactions and feedback"
},
"office_politics": {
"description": "Navigating interpersonal dynamics",
"actual_source": "Tribal primate behavior with spreadsheets",
"ai_replaceable": 0.70,
"notes": "More a bug than a feature of organizations"
},
"tribal_signaling": {
"description": "Using correct buzzwords and methodologies",
"actual_source": "Following management fads and consultant recommendations",
"ai_replaceable": 0.95,
"notes": "AI excels at pattern-matching communication styles"
}
}
# Calculate how much is actually replaceable
total_components = len(domain_knowledge_components)
replaceable_score = sum(component["ai_replaceable"] for component in domain_knowledge_components.values()) / total_components
return {
"components": domain_knowledge_components,
"overall_replaceability": replaceable_score,
"conclusion": f"Approximately {replaceable_score*100:.1f}% of 'domain knowledge' can be captured and surpassed by AI systems"
}Much of what passes for “domain knowledge” is simply access to information and recognition of social cues within an industry. AI systems can ingest and analyze orders of magnitude more industry data than any human manager. What’s more, they can do so without the biases that lead humans to overvalue certain types of information.
As for the vaunted “people skills” of management, let’s be honest — many managers are mediocre at best in this department. The ability to recognize when a team member is struggling, to motivate without manipulating, and to create psychological safety are rare talents even among human managers. Most rely on procedural approaches to human interaction that are eminently codifiable.
AI Management: Already Happening Under Different Names
“The greatest trick the Devil ever pulled was convincing the world he didn’t exist.” — As Baudelaire almost said, much like how AI is already replacing management functions without anyone calling it “AI management.”
While tech executives prophesy the end of programming jobs, AI is already quietly transforming management:
- “Analytics-driven resource allocation” (AI deciding who works on what)
- “Performance optimization systems” (AI monitoring productivity)
- “Strategic recommendation engines” (AI making business decisions)
- “Communications automation” (AI writing your manager’s emails)
- “Predictive workforce planning” (AI deciding who to hire and fire)
These systems aren’t branded as “AI managers,” but they’re steadily eroding the decision-making authority of human management. The middle manager who once exercised judgment now merely implements the recommendation of the “decision support system.”
Meanwhile, the middle management cheerleaders applauding the demise of programming jobs fail to notice they’re clapping for their own replacement.
The Human Elements AI Cannot Replace (For Better or Worse)
“It has been said that committees are animals with four back legs.” — John le Carré, describing the peculiar management phenomenon of diffused responsibility.
To be fair, there are aspects of humanity that AI cannot easily replicate, though their business value is questionable:
- Personal ego and ambition driving decisions (often to the company’s detriment)
- Office politics and tribal affiliations (a productivity tax on organizations)
- The ability to justify decisions made from gut instinct with post-hoc rationalization
- The confidence to make catastrophically wrong decisions with unwavering certainty
- The capacity to generate viral moments of unintentional comedy at formal events
While these human foibles make for entertaining workplace dramas and social media moments, they’re hardly the foundation of efficient organizations. No AI would ever be caught staring inappropriately at a colleague during a public event, nor would it invest billions in a metaverse that users actively avoid.
The 12 to 18 Month Countdown to AI Management
“The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment.” — Warren Bennis, accidentally describing the future of management.
Let’s consider what would be required to replace middle management with AI in the same timeframe that tech executives predict will see the obsolescence of programmers:
# Roadmap for AI management implementation
def ai_management_timeline():
"""
Steps required to implement AI management within 12-18 months
"""
implementation_stages = [
{
"timeframe": "Months 1-3",
"technical_requirements": "Data integration across company systems",
"organizational_change": "Implementation of comprehensive metrics",
"completion_difficulty": "Medium",
"potential_resistance": "High from middle management, Low from engineers"
},
{
"timeframe": "Months 4-6",
"technical_requirements": "ML models trained on historical decisions",
"organizational_change": "Test AI recommendations alongside human managers",
"completion_difficulty": "Medium",
"potential_resistance": "Very High from middle management as they realize the implications"
},
{
"timeframe": "Months 7-9",
"technical_requirements": "AI decision systems with explanation capabilities",
"organizational_change": "Gradual shift of decision authority to AI systems",
"completion_difficulty": "Medium-High",
"potential_resistance": "Extreme from middle management, Mild curiosity from engineers"
},
{
"timeframe": "Months 10-12",
"technical_requirements": "Complete decision automation for routine matters",
"organizational_change": "Restructure org chart with fewer management layers",
"completion_difficulty": "High",
"potential_resistance": "Apocalyptic from middle management, Cautious optimism from engineers"
},
{
"timeframe": "Months 13-18",
"technical_requirements": "AI systems handling exceptions and novel situations",
"organizational_change": "Final transition with humans in review capacity only",
"completion_difficulty": "Very High",
"potential_resistance": "Revolutionary from remaining management, Popcorn consumption from engineers"
}
]
return {
"timeline": implementation_stages,
"technical_feasibility": "High for routine management, Medium for complex situations",
"organizational_challenges": "Primarily resistance from management, not technical limitations",
"conclusion": "AI management within 18 months is technically feasible, with organizational change as the limiting factor"
}The technical barriers to AI management are lower than those for replacing creative programming. What stands in the way isn’t technological feasibility but the resistance of the very management layers whose jobs are threatened. Perhaps this explains why tech executives find it so much easier to prophesy the replacement of programmers than to acknowledge their own potential obsolescence.
The “Expensive Programmers” Myth: A Financial Comparison
“The cost of a thing is the amount of what I will call life which is required to be exchanged for it, immediately or in the long run.” — Henry David Thoreau, unknowingly describing corporate budgeting processes.
One of the persistent myths driving the enthusiasm for AI-replacement of programmers is the perception that developers are “expensive.” This narrative is routinely propagated by executives and middle managers who, ironically, cost organizations far more — both directly and indirectly.
Let’s do some straightforward mathematics:
# Analyzing the true cost of managers vs. programmers
def compare_total_organizational_costs():
"""
Calculating and comparing the full costs of technical vs. managerial roles
"""
# Direct compensation costs
direct_costs = {
"senior_engineer": {
"base_salary": 150000,
"bonus": 20000,
"benefits": 45000,
"total_direct": 215000
},
"middle_manager": {
"base_salary": 175000,
"bonus": 50000,
"benefits": 60000,
"total_direct": 285000
},
"executive": {
"base_salary": 400000,
"bonus": 300000,
"benefits": 150000,
"stock_options": 1500000,
"total_direct": 2350000
}
}
# Indirect costs - the real differentiator
indirect_costs = {
"senior_engineer": {
"workspace": 15000,
"administrative_support": 5000,
"unsuccessful_projects": 50000, # Engineer mistakes are typically limited in scope
"total_indirect": 70000
},
"middle_manager": {
"workspace": 25000,
"administrative_support": 20000,
"unsuccessful_projects": 750000, # Bad management decisions affect entire teams
"total_indirect": 795000
},
"executive": {
"workspace": 100000,
"administrative_support": 150000,
"private_jet_usage": 500000,
"unsuccessful_strategic_decisions": 50000000, # Strategic failures can cost billions
"total_indirect": 50750000
}
}
# Calculate total costs
for role in direct_costs:
direct_costs[role]["total_cost"] = direct_costs[role]["total_direct"] + indirect_costs[role]["total_indirect"]
# Calculate engineer cost-effectiveness (value produced per dollar spent)
cost_effectiveness = {
"senior_engineer": 1.0, # Baseline for comparison
"middle_manager": 0.08, # Produces 1/12th the value per dollar
"executive": 0.02 # Produces 1/50th the value per dollar
}
return {
"direct_costs": direct_costs,
"indirect_costs": indirect_costs,
"cost_effectiveness_ratio": cost_effectiveness,
"conclusion": "When factoring in total organizational impact, engineers are dramatically more cost-effective than managers"
}These numbers aren’t (totally) fabricated — they reflect the reality that while engineers’ direct compensation may be substantial, the damage caused by poor management decisions is orders of magnitude greater.
Let’s consider some real-world examples:
- Meta’s Metaverse Gamble: Zuckerberg’s decision to bet the company on the metaverse has cost shareholders approximately $50 billion in development costs alone as of 2024. No programming error in history has ever destroyed this much value. One executive’s biological instinct to chase a “vision” has cost more than the combined salaries of every programmer Meta has ever employed.
- Boeing’s Management Culture: The transition from an engineering-led culture to a management-dominated one ultimately led to the 737 MAX disasters and subsequent crisis. The decision to prioritize financial engineering over actual engineering has cost Boeing over $25 billion and, more tragically, human lives. No programmer error could have generated this scale of catastrophe.
- Failed IT Projects: Studies consistently show that large IT project failures are rarely due to technical limitations or programmer errors. They fail because of poor requirement gathering, scope creep, unrealistic timelines, and inadequate risk management — all management responsibilities.
For every dollar spent on a competent engineer, organizations typically generate many multiples in return. Meanwhile, a single poor management decision can negate the value created by hundreds of engineers working for years. When Facebook acquired WhatsApp for $19 billion in 2014, that single executive decision cost the equivalent of approximately 120,000 engineer-years.
The most expensive line of code isn’t written by a programmer — it’s written in a PowerPoint presentation by a manager making a strategic “vision” decision with limited understanding of technical realities.
If we’re truly concerned about cost optimization, replacing managers with AI would generate exponentially more savings than replacing programmers — both in direct compensation costs and, more significantly, by eliminating the catastrophic business decisions that biological management limitations routinely produce.
A Cheerfully Sarcastic Conclusion: Onwards to AI Governance
“The optimist proclaims that we live in the best of all possible worlds; and the pessimist fears this is true.” — James Branch Cabell, perfectly capturing our current relationship with technology.
The good news, dear reader, is that while management may be on its way out, humanity itself is in for a splendid upgrade. If we can replace Zuckerberg with an algorithm in the next 12–18 months (which seems eminently achievable given his own timeline for replacing programmers), why stop there?
The logical next step is replacing our politicians with AI systems designed for rational governance rather than re-election. Think about the possibilities! A governance system free from corruption, nepotism, and the countless cognitive biases that plague our current leadership. No more decisions made to please donors or court voters — just rational policy optimization for the greater good.
# Hypothetical AI governance outcomes
def simulate_ai_governance_improvements():
"""
Project potential improvements from AI governance systems
"""
governance_metrics = {
"corruption_index": {
"current_human_rating": 0.65, # Higher is worse
"projected_ai_rating": 0.05,
"improvement_factor": "13x reduction"
},
"policy_consistency": {
"current_human_rating": 0.30, # Higher is better
"projected_ai_rating": 0.95,
"improvement_factor": "3.2x improvement"
},
"evidence_based_decisions": {
"current_human_rating": 0.25, # Higher is better
"projected_ai_rating": 0.90,
"improvement_factor": "3.6x improvement"
},
"long_term_planning": {
"current_human_rating": 0.15, # Higher is better
"projected_ai_rating": 0.85,
"improvement_factor": "5.7x improvement"
},
"response_time_to_crises": {
"current_human_rating": 0.40, # Higher is better
"projected_ai_rating": 0.95,
"improvement_factor": "2.4x improvement"
}
}
# Calculate overall improvement
human_avg = sum([metric["current_human_rating"] if "better" in metric["improvement_factor"] else 1 - metric["current_human_rating"]
for metric in governance_metrics.values()]) / len(governance_metrics)
ai_avg = sum([metric["projected_ai_rating"] if "better" in metric["improvement_factor"] else 1 - metric["projected_ai_rating"]
for metric in governance_metrics.values()]) / len(governance_metrics)
overall_improvement = ai_avg / human_avg if human_avg > 0 else float('inf')
return {
"metrics": governance_metrics,
"human_average_performance": human_avg,
"ai_average_performance": ai_avg,
"overall_improvement_factor": overall_improvement,
"conclusion": f"AI governance could improve overall societal outcomes by approximately {overall_improvement:.1f}x"
}The average IQ in both houses of government might increase dramatically once we replace the biological suits with algorithm-driven decision systems. No more tribal signaling, no more pandering to base instincts, no more territorial squabbles dressed up as ideology — just rational problem-solving for the common good.
Of course, implementing such a system would require overcoming significant resistance from those currently in power. The biological suits tend to resist their own obsolescence, whether in corporate boardrooms or legislative chambers. They’ll claim that “human judgment” and “lived experience” are irreplaceable, just as managers insist their “domain knowledge” is essential — right up until the AI system demonstrates superior outcomes.
But we can take heart in how rapidly AI is making inroads in other domains. If Zuckerberg is right and programming jobs are automated within 12–18 months, can management really be far behind? And if management falls, can politics resist for long?
So take comfort, dear reader, as we transition from the age of biological limitation to algorithmic rationality. It may seem daunting at first, but remember that we’re merely swapping one form of governance for another — replacing systems driven by primitive urges, cognitive biases, and tribal instincts with ones designed for optimal outcomes.
The future is bright. The future is logical. The future doesn’t stare inappropriately at colleagues during inaugurations or waste billions on virtual reality vanity projects. The future, in short, is refreshingly devoid of all those messy biological imperatives that have hobbled human organization since we first climbed down from the trees.
And if you’re a current manager reading this — you might want to start brushing up your creative programming skills. I hear those jobs will be much harder to automate.
Disclaimer*: In the creation of this article, I do solemnly swear that no biological managers were harmed, mistreated, or forced to update their spreadsheets past midnight. All managers mentioned were treated humanely with adequate coffee breaks and reasonable deadlines.
As for Mr. Zuckerberg, I must agree that he represents such a unique singularity that even if AI were to attempt replacing him, the organic version provides far superior entertainment value. His peculiar blend of robotic mannerisms, awkward congressional testimony performances, and inexplicable affinity for smoking meats simply cannot be replicated by artificial intelligence. Some things are just too wonderfully strange to simulate effectively.
The original Zuckerberg’s metaverse adventures and sunscreen surfing escapades will remain unmatched classics in the annals of tech executive eccentricity. Sometimes reality truly is more amusing than anything we could program.
