The Economic Reality of AI Means Scale Matters

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.

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."

What really grabbed our attention in this study was how the decision to automate hinges on various assumptions about costs and benefits. The biggest cost factors turn out to be the expenses related to developing the AI (engineering costs) and obtaining the necessary data, along with how accurate the AI needs to be. Surprisingly, the cost of cloud services didn't weigh as heavily. To put it another way, if you can get your hands on data for free and the price of building the AI system is low, especially if you only need the AI to be reasonably accurate, then automation becomes a much more appealing option.

On the flip side, the benefits from automation don't follow a straightforward path. For instance, if an AI system can do twice as much as a human, it doesn't mean twice as many tasks become worth automating—the increase is only from 23% to 30% of tasks. To see a significant jump in what tasks are worth automating, the benefits provided by AI need to skyrocket, not just improve steadily.

This study brings to light a few crucial insights for investors and business leaders: AI's value multiplies as a platform. The crucial factor is how broadly you can deploy AI, looking beyond mere labor automation to leveraging AI systems more extensively, and finding cost-effective methods to develop AI technologies.

For investors, considering AI-as-a-service is a viable route, yet they should be mindful of the potential costs tied to customization and fine-tuning. Given that job tasks vary widely across different sectors, even slight development expenses could deter widespread adoption, necessitating a significant effort in sales and coordination. On the computational front, an investor's stance on the evolution of scaling laws in foundational AI models could critically influence the viability of their investment.

For business leaders, the critical consideration is the breadth and impact of AI's advantages. Question whether the AI enhances human decision-making within their operational framework or if it amplifies a specific expertise across broader applications, such as enabling additional operational shifts without increasing labor costs. Also, evaluate your strategy for developing cost-effective AI technologies. Despite the promise of foundational models, the necessity for fine-tuning remains a significant variable. Moreover, vision-based AI doesn't adapt as seamlessly across different settings as language models do, often requiring more annotated data. This distinction is crucial, especially considering that companies generally possess more text data than visual data. For a deeper dive into advancements in this area, particularly Apple's contributions, refer to our analysis in "Eyes on Apple".

Where might this paper have missed the mark? We wonder about two things: the paper's take on AI costs amid the rise of AI-powered mobile devices, and the complex decisions involved in applying vision technology.

AI is increasingly becoming part of smaller systems and integrating into existing infrastructures. The advent of digital twins exemplifies how incorporating computer vision can transform task configurations from the ground up, potentially introducing benefits like enhanced precision and safer environments—advantages that might not have been fully considered in the study. As the cost of making predictions drops, new decision-making and actions become feasible, potentially amplifying the value by paving the way for the reimagining of entirely new operational processes.

We're also curious about the researchers' approach to assessing industries' exposure to computer vision. Having delved deep into ONet data ourselves, we appreciate its intricacy. The report's concluding graph, titled "Computer vision exposure and economic feasibility for compensation within individual sectors," caught our eye. Retail sits at the top for exposure/feasibility, which aligns with our findings. However, Mining and Utilities are at the bottom, raising eyebrows given the numerous successful computer vision applications in these fields. Could it be these sectors are already benefiting from AI, skewing the results? That doesn't quite fit either. Or, is the lower ranking due to fewer employees in these sectors compared to retail? That seems more plausible. Yet, we suspect there's more to the story.

We hold this study in high regard for tackling a crucial aspect of the automation narrative—the economic calculus behind AI adoption. It underscores the complexity of AI, demonstrating that supplanting or even enhancing human capability with technology is no small feat. This research nudges us towards a deeper comprehension of the evolving "World of Workflows," reminding us that we're just beginning to figure this out.