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.
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.
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."