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Kelsi Guidry – Bizer Founder

Podcast – How the New AI Economic Machine Works

This is a factual global explainer for small business owners and entrepreneurs. It describes how artificial intelligence is changing productivity, capital formation, labor, energy demand, and policy across the world economy.

The Basic Engine: Productivity Drives Growth

Every economy expands through three fundamental drivers: labor, capital, and productivity. Artificial intelligence primarily influences the third driver by increasing output per hour in knowledge and service-based tasks, while also enhancing the quality, efficiency, and speed of workflows across industries.

The Financial Stability Board notes that AI has the potential to lift productivity at both the firm and industry level, feeding into higher output and incomes over time. This is the same channel that powered past technological revolutions, from mechanization to the internet.

McKinsey & Company provides one of the most comprehensive estimates of this impact. Its 2023 report The Economic Potential of Generative AI projected USD 2.6 trillion to USD 4.4 trillion in annual global value creation across 63 practical use cases. The firm’s more recent research confirms that this potential is beginning to materialize:

  • In The State of AI in 2024, McKinsey found that 65 percent of organizations were using generative AI regularly, up sharply from the prior year.
  • In its 2025 update AI in the Workplace, McKinsey reaffirmed the same USD 4.4 trillion productivity opportunity, but shifted emphasis from potential to realization, noting that firms are now scaling AI into core workflows.

Together, these studies demonstrate that AI’s impact on the global economy is shifting from projected potential to tangible results. Productivity gains—once merely hypothetical—are now being realized in real organizations across diverse sectors and regions.

(McKinsey Global Institute, 2023)
(McKinsey, The State of AI in 2024)
(McKinsey, AI in the Workplace: 2025)

Where the Money Is Flowing

AI is redirecting global capital toward computing infrastructure, data, and software.

Semiconductors. Global chip sales are rising on the strength of logic and memory used for AI training and inference. The World Semiconductor Trade Statistics (WSTS) projects the market to reach roughly 700–728 billion USD in 2025, with double-digit annual growth tied to cloud and AI demand.
(WSTS, 2024)

Data centers and infrastructure. Hyperscale cloud providers have raised capital expenditure more than 30 percent year-over-year in 2025, according to Dell’Oro Group, as they build new AI clusters and retrofit existing sites. Construction and energy reports show record activity but also rising grid constraints.
(Dell’Oro Group, 2025)

Venture investment. Global private AI investment climbed from single-digit billions in 2012 to tens of billions by 2020, with the United States and China capturing most flows. OECD AI Policy Observatory (2025) continues to track heavy concentration of funding in a few technology hubs.
(OECD AI Policy Observatory, 2025)

Labor and Skills: What Changes and Why

AI affects labor through substitution, complementarity, and new task creation. The International Monetary Fund (IMF) estimates that about 40 percent of global jobs are exposed to AI, with higher exposure in advanced economies where cognitive work dominates. Many roles will evolve rather than disappear, increasing demand for digital and analytical skills.
(IMF Staff Discussion Note 2024/002)

Empirical studies cited by the Federal Reserve Bank of Dallas (2025) show that AI access raises worker productivity, especially for less-experienced employees who benefit from assistance on repetitive tasks. Broader adoption could lift living standards if training keeps pace.
(FRB Dallas, 2025)

Energy and Physical Constraints: The New Bottlenecks

Computing power requires electricity and cooling. The International Energy Agency (IEA) projects that data-center electricity use could nearly double by 2030 to around 945 terawatt-hours, with AI-optimized facilities accounting for much of the increase.
(IEA Data Centre Outlook 2024)

Commercial-real-estate analyses from JLL (2025) report record construction but note grid connection and permitting delays that raise costs and slow deployment. Energy and land availability are now critical economic variables in AI expansion.
(JLL Data Center Report 2025)

What Governments and Institutions Are Trying to Achieve

Policy objectives are converging on two goals:

1. Capture productivity gains. OECD research links higher AI adoption to higher labor productivity and quality improvements across firms of all sizes. Governments are investing in digital infrastructure and small-business diffusion programs.
(OECD Digital Economy Outlook 2024)

2. Manage risk and inequality. The IMF and World Bank highlight the need for education, reskilling, and competition policy to prevent concentration of power and to keep markets open to innovation.
(IMF World Economic Outlook 2024)

Energy and environmental policy now intersect with digital growth. The IEA calls for coordinated planning on generation, transmission, and efficiency to balance AI energy demand with climate goals.

How This Plays Out in the Real Economy

The AI economy operates as a reinforcing sequence:

  1. Model quality improves, raising the return on compute and data.
  2. Capital shifts toward chips, data centers, and AI-native software.
  3. Adoption spreads across firms and sectors, lifting task-level productivity.
  4. Labor reorganizes as tasks change and skills evolve.
  5. Energy and component bottlenecks influence cost and geography.
  6. Policy responses determine diffusion speed and guardrails.

Each loop reinforces the next. Rising adoption drives investment, investment lowers costs, and lower costs enable wider use across industries and regions.

What This Means for Small Business Owners and Entrepreneurs

For small firms, the macro cycle translates into practical steps.

Expect capability costs to fall. As chip production scales and infrastructure expands, AI tools become cheaper and easier to use. OECD data show rapid diffusion of affordable AI services that improve accuracy and output per hour.

Monitor input markets. Chips, cloud compute, and electricity determine AI operating costs. Reports from JLL and the IEA highlight tight grids in some regions, which can affect latency and pricing for cloud services.

Invest in human capital. The greatest productivity gains come when people and AI work together. Research from the Federal Reserve Bank of Dallas shows significant improvements when workers learn to use AI in daily tasks. Prioritize training, prompt libraries, and workflow redesign.

Follow capital flows to find opportunity. Growth continues in semiconductor supply chains, data-center development, applied AI software, and power-system innovation. These are the ecosystems forming around the new machine.

The Bigger Picture

Society’s long-term aim is to use AI to increase global productivity, free human time for higher-value work, and expand prosperity without exhausting resources. The outcome depends on balance — between innovation and regulation, computation and sustainability, efficiency and inclusion.

As this new machine matures, small business owners who understand its parts will see where value is moving and position themselves early in the next phase of economic growth.

Verified Sources

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Kelsi Guidry – Bizer Founder

I’m the founder of Bizer. I’m an outdoorsy entrepreneurial web geek.

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