It is quite understandable that financial analysts covering public firms are concerned about the payback period for various forms of artificial intelligence. For example, venture capitalist David Cahn with Sequoia Capital argues that the big hyperscale cloud computing companies must earn about $600 billion in revenue to justify their investments in AI infrastructure, focused only on graphics processor investments and data center facilities and operating costs, plus an expected 50-percent profit margin on software sales.
That noted, Cahn also says “a huge amount of economic value is going to be created by AI. Company builders focused on delivering value to end users will be rewarded handsomely,” as AI is a potentially “generation-defining technology wave.”
The larger point is that speculative frenzies are part of technology deployment. “Those who remain level-headed through this moment have the chance to build extremely important companies,” says Cahn. “But we need to make sure not to believe in the delusion that has now spread from Silicon Valley to the rest of the country, and indeed the world.”
In other words, the “get rich quick” mentality is going to disappoint, as did the mid-1880s gold rush in California.
So will there be an AI investment bubble? Yes, he might argue. Such periods of investment frenzy have happened in the past, as well, before the benefits were realized.
Engines That Move Markets: Technology Investing from Railroads to the Internet and Beyond by Alasdair Nairn describes the recurring investment patterns associated with major technological advancements. He notes that these innovations often follow a cycle, moving from skepticism to enthusiasm. Lots of venture capital investment follows, accompanied by inflated stock prices.
Eventually, as the technology matures and financial realities set in, many companies fail, stock prices collapse, and naive investors lose money.
If the railroad investment pattern holds, there could be disappointment. Over the long term, investments in railways were not rewarding, he argues. Despite their economic impact, railways provided negative investment returns in real, relative, or absolute terms, however important the economic contribution.
The point is that it is a safe bet to argue AI overinvestment will occur. That tends to be the pattern for major new technologies, especially those we generally recognize as being general-purpose technologies with wide economic impact.
After all, huge capital investments in graphics processor units, for example, must be reflected in revenue upside at some point. The issue is whether expectations of near-term return are actually reasonable.
As always, market forecasters, firm executives and others might lean towards the strategic implications, while financial analysts primarily look at the quarterly performance metrics.
And, sometimes, investments are more “strategic” than “tactical.” In other words, a telco might have to invest heavily in fiber-to-home facilities simply to stay in business as competitors upgrade their home broadband infrastructures.
The actual financial return on investment will matter, but might not be the driver. “You get to keep your business” or “you get to stay in business” might be the value, not simply increases in revenue after the investments are made.
Most new information technologies take some time before we tend to see measurable benefits. That has been true for many technologies. So the issue is whether various forms of AI are more like social media or smartphones or PCs, the internet and automated teller machines.
Technology | Approximate Lag Time to Measurable Outcomes (Years) |
PCs | 10-15 |
Internet | 10-15 |
ATMs | 8-10 |
HDTV | 5-8 |
Cloud Computing | 5-7 |
Wireless/Wi-Fi | 5-7 |
Smartphones | 3-5 |
Social Media | 3-5 |
Applying various forms of AI to various use cases across industries might reasonably produce varied payback periods, from rapid to lengthy, suggesting that investment tied to particular use cases is a reasonable approach.
Most of us likely can imagine clear performance benefits in areas ranging from e-commerce, search and social media recommendations fairly quickly. As AI already is used to support such personalization features.
Other use cases, including manufacturing or healthcare, might take longer, in part because many parts of the value chain have to be altered at the same time to take advantage of AI.
Industry/Use Case | Estimated Payback Period |
E-commerce (general) | 1.2 - 1.6 years |
E-commerce (search optimization) | 6 months - 1 year |
Social Media & Content | 1 - 2 years |
Manufacturing | 2 - 3 years |
Business Services (Law, Accounting, Consulting) | 1.5 - 2.5 years |
Smartphones (AI features) | 1 - 2 years |
PCs (AI-enhanced software) | 2 - 3 years |
Cloud Computing (AI services) | 1 - 2 years |
Healthcare (AI diagnostics) | 2 - 4 years |
Financial Services (Fraud Detection) | 1 - 1.5 years |
Obviously there are many variables. Larger-scale implementations may see faster payback due to economies of scale, so long as they are targeting major functions that can affect financial return.
Some AI applications, such as fraud detection in financial services, may see quicker returns compared to more complex implementations in healthcare or manufacturing, and also be easier to measure.
Existing information technology infrastructure and past success integrating information technologies, probably also will matter. Companies that have more-developed IT might see faster payback periods compared to firms whose existing infra is less well developed.
Fast-moving industries such as e-commerce and social media might realize benefits quicker than more traditional sectors, simply because they face fewer regulatory issues that must first be addressed.
Regulatory environment: Industries with strict regulations (e.g., healthcare, finance) may have longer payback periods due to compliance requirements.
As always, the particular use cases will have different payback periods, when implemented at scale.
Industry | AI Use Case | Payback | Source |
E-commerce | Product Recommendation Engines | 6-12 months | McKinsey, AI in Retail |
Social Media & Content | Personalized Content & Ad Targeting | 12-18 months | Forrester, AI in Marketing |
Manufacturing | Predictive Maintenance | 18-24 months | PwC, AI in Manufacturing |
Business Services | Legal Document Review & Due Diligence | 24-36 months | Accenture, AI in Professional Services |
Smartphones | Voice Assistants & Virtual Companions | 3-5 years (Long-term brand value) | CB Insights, AI in Mobile |
PCs & Cloud Computing | Resource Optimization & Server Management | 12-18 months | Bain, AI in Cloud Computing |
