The Economic Reality of AI Means Scale Matters

A recent study from MIT has been grabbing attention with its unconventional take: don't worry about AI snatching your job, it's not cost-effective.

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Key Points:

  • Study Highlights AI Cost-Effectiveness: A recent MIT study reveals that only 23% of jobs at risk from AI automation are actually worth automating, challenging the notion that AI will widely replace human jobs.
  • Focus on Machine Vision: The study uses machine vision as a focal point due to its abundant data on cost-effectiveness, highlighting that 77% of vision tasks are not cost-effective to automate at the firm level.
  • Role of Scale in AI Deployment: The critical role of scale is emphasized, showing that AI is cost-effective primarily for large corporations or through AI-as-a-service models, making it viable for broader applications.
  • High Costs of AI Development: The biggest cost factors in AI automation are related to developing the AI and obtaining the necessary data, while the cost of cloud services is less significant.
  • AI Adoption Limited to Large Firms: Less than 6% of companies have adopted AI, although these firms account for 18% of jobs, highlighting the limited but impactful adoption of AI.
  • Implications for Investors and Business Leaders:
    • Investors: AI-as-a-service is a viable investment route, but customization and fine-tuning costs need consideration. Understanding the evolution of scaling laws in AI models is crucial.
    • Business Leaders: Focus on how AI enhances decision-making and operational efficiency. Evaluate the cost-effectiveness of AI technologies and the necessity for fine-tuning, especially in vision-based AI applications.

A recent study from MIT has been grabbing attention with its unconventional take: don't worry about AI snatching your job, it's not cost-effective. The headlines highlight that only 23% of jobs at risk from AI are actually worth automating. While that's the headline-grabber, the study delves into more nuanced insights.

Google search for "automation job loss" Jan 24

In 2016, we conducted our own study into labor automation, similar to the well-known Oxford study, which made waves with its claim that 47% of US jobs might be automated, and a related study by McKinsey. (For more details on our methodology, findings, and the key insights required to grasp these studies, click here). Each study comes with its own set of limitations, stemming from the assumptions researchers must make about AI's future development, the nature of jobs, and the economic factors influencing the adoption of AI.

This study stands out as the first comprehensive academic study to construct a full-fledged model of AI automation. It delves into the proficiency required for specific tasks, the associated costs to attain such proficiency (whether by machine or human), and the economic considerations driving the decision to embrace the technology. The focus on machine vision was strategic, chosen for its wealth of data on cost-effectiveness.

There are two big takeaways from the study:

  • 77% of vision tasks are not cost effective to automate if a system is only used at the firm level, and,
  • the only way to make AI cost effective for most jobs in the USA is to have a single system—either the firms have to get larger through market share gains or the AI has to scale through formation of AI-as-a-service.

The key takeaway from this study isn't just the 77% figure: it's the critical role of scale in AI deployment. This research reveals that the high cost of AI makes it viable primarily for large corporations. The authors' findings shed light on why AI adoption is limited to less than 6% of companies, which, however, account for 18% of jobs due to their size. Surprisingly, the average worker is employed by a company where automating vision tasks doesn't present a cost-effective option. To put it into perspective, "Even a hypothetical firm as big as Walmart lacks the scale to make automating 15% of their vision tasks attractive."

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