Are We in an AI Bubble? A Tech Professional's Perspective

TLDR: We're witnessing unprecedented AI investment and hype. Historical patterns suggest this looks like a bubble, but that doesn't mean AI isn't revolutionary. Smart engineers should stay engaged but cautious, avoiding career decisions based purely on FOMO.

The Current Landscape

As software engineers, we're living through what might be the most significant technological shift of our careers. Companies are pouring unprecedented capital into AI infrastructure, with spending levels that rival entire national budgets.

The Numbers Are Staggering:

• AI accounts for ~90% of S&P 500 capex growth
• Nvidia went from $600M market cap (1999 IPO) to briefly touching $5 trillion
• Multiple AI startups raising $1B+ seed rounds with no product

What Makes This Time Different (And What Doesn't)

The "This Time It's Different" Arguments

As developers, we hear these daily:

• Real products with real users: ChatGPT hit 1 billion users faster than any technology in history
• Immediate revenue: Companies like Anthropic seeing 10x YoY revenue growth
• Proven leaders: Microsoft, Google, Meta aren't dot-com startups
• Reasonable valuations: Current AI leaders' P/E ratios are lower than 1999 tech stocks

The Historical Echoes

But experienced engineers recognize familiar patterns:

1860s: Railroads
1920s: Radio & Aviation  
1990s: Dot-com
2000s: Subprime housing
2020s: AI?

Each revolution followed the same arc:

1. Revolutionary technology emerges
2. Early adopters make fortunes
3. FOMO drives everyone else in
4. Overbuilding and speculation
5. Crash (but technology persists)

The Technical Reality Check

What We Know

• LLMs are genuinely transformative
• Coding assistants (Claude Code, Cursor) are already replacing significant developer hours
• GPU demand is real and growing
• The technology works and is improving rapidly

What We Don't Know

• Who will be the ultimate winners
• Whether first-movers can maintain advantages
• If AI services become commoditized (price competition → thin margins)
• Economic lifespan of AI infrastructure
• Whether AGI is achievable (and on what timeline)

The Circular Money Problem

As engineers, we should be skeptical of "circular deals":

// Simplified illustration of circular revenue problem
public class CircularDeal {
    // Company A invests $1B in Company B
    companyA.invest(companyB, 1_000_000_000);

    // Company B uses that $1B to buy services from Company A
    companyB.purchaseServices(companyA, 1_000_000_000);

    // Both companies report revenue
    // But where's the real value creation?
}

Historical parallel: 1990s telecom companies buying fiber from each other, funded by vendor financing. It inflated revenues without real demand.

Current concerns:

• OpenAI receiving billions from tech companies
• OpenAI paying billions back to those same companies for compute
• Nvidia investing in customers who buy Nvidia chips
• Goldman estimates 15% of Nvidia's revenue comes from such arrangements

The Debt Factor: A Developer's Perspective

Tech companies traditionally funded growth through equity or cash flow. Now:

• Microsoft, Google, Meta issuing 30-year bonds for AI
• Startups borrowing billions for data centers
• SPVs (Special Purpose Vehicles) hiding debt off balance sheets

The Risk: In software engineering, we understand that:

public class InvestmentRisk {
    /**
     * Risk Amplification Formula
     * When outcomes are uncertain, debt amplifies both gains AND losses
     */
    public double calculateRisk(double uncertainty, double debtLeverage) {
        return uncertainty * debtLeverage; // Exponentially higher risk
    }
}

AI outcomes are highly uncertain. Adding leverage amplifies both potential gains AND losses.

What This Means for Your Career

Jobs That Are Already Changing

• Junior coding positions: AI can now write production-quality code
• Documentation writing: Automated by LLMs
• Basic code review: Increasingly automated
• Junior legal/analyst work: Pattern matching that AI excels at

Jobs That Remain Valuable

• System architecture: Requiring holistic judgment
• Product decisions: Requiring user empathy
• Team leadership: Requiring emotional intelligence
• Debugging complex systems: Requiring deep contextual understanding
• Physical infrastructure: Can't be automated (yet)

The "Skill Obsolescence" Risk

One concerning pattern: If we eliminate junior positions, where do senior experts come from? You don't become a 10x engineer without years of practice.

Practical Advice for Engineers

DO:

1. Learn AI tools: They're productivity multipliers
2. Stay informed: Technology is real, even if valuations aren't
3. Maintain fundamentals: Core CS skills remain valuable
4. Diversify: Don't bet entire career on AI hype
5. Watch for warning signs: Vague promises, circular revenue, unrealistic timelines

DON'T:

1. Join companies purely for AI hype: Fundamentals still matter
2. Ignore traditional skills: Algorithms, systems design, etc.
3. Panic about job loss: Transitions take time
4. Assume current leaders will win: Remember Netscape, Yahoo, MySpace
5. Believe "$1T valuation" stories: These often end badly

The Infrastructure Perspective

As someone who's built systems at scale, I'm watching the data center buildout with concern:

Current trajectory:

• JP Morgan estimates $5T needed for AI infrastructure
• Companies' cash reserves: ~$350B
• Gap must be filled with... debt

Historical parallel:

• 1990s: Overbuilt fiber optic networks
• Early 2000s: Massive telecom bankruptcies
• Long-term: Infrastructure was used, but investors lost billions

The technology was real. The overinvestment was also real.

Questions to Ask Your Employer

If you're evaluating AI companies or roles:

1. Revenue model: "How do you make money?" (Be skeptical of vague answers)
2. Burn rate: "How long is your runway?"
3. Differentiation: "What happens when OpenAI/Google does this?"
4. Debt load: "How much is borrowed vs. equity?"
5. Customer concentration: "Is revenue real or circular?"

The Optimistic Case

Despite concerns, reasons for hope:

1. Baby boomers retiring: 16M retiring by 2035, AI fills gaps
2. Productivity gains: Real time savings for developers
3. New problem spaces: AI might create more jobs than it destroys
4. Pattern breaks: 20% of the time, "this time IS different"

Conclusion: The Middle Path

Neither blind optimism nor pessimism serves engineers well.

My recommendation:

/**
 * AI Career Strategy Pattern
 */
public class AICareerStrategy {

    private static final String ENGAGEMENT_LEVEL = "MODERATE"; // Not zero, not all-in

    public String execute() {
        // Stay informed and skilled
        learnAITools();
        maintainFundamentals();

        // Make decisions based on fundamentals
        if (hasRealRevenue() && hasReasonableValuation()) {
            considerOpportunity();
        }

        // But don't bet everything
        avoidAllInBets();

        return "STAY_FLEXIBLE_AND_INFORMED";
    }

    private void learnAITools() {
        // Stay current with AI developments
    }

    private void maintainFundamentals() {
        // Keep core CS skills sharp
    }

    private boolean hasRealRevenue() {
        return true; // Verify actual revenue model
    }

    private boolean hasReasonableValuation() {
        return true; // Check P/E ratios, fundamentals
    }
}

We're witnessing either the most important technological revolution of our lifetimes, or the most spectacular bubble, or (most likely) both simultaneously.

The technology is real. The hype is also real. Navigate accordingly.

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Discussion Questions

What's your experience with AI in your daily work? Are you seeing real productivity gains or just hype? How is your company approaching AI investment?

Tags: #ai #career #bubble #machinelearning #investing #technology #opinion

Comments

[david duymelinck]

What makes you conclude AI is an important technological revolution?