Since artificial intelligence is so new as a driver of firm revenues, growth and valuation, professionals have few ways to model AI equity reward and risk, as was true for professionals in the early days of the internet.
Traditional valuation methods, such as price-to-earnings (P/E) ratios and discounted cash flow (DCF) analysis for firms with negative earnings or a clear path to profitability.
Instead, assessments will have to turn on other more-subjective angles, such as business model “potential," traffic or user engagement, much as early internet-watchers had to rely on the number of unique visitors, the average time spent on site, and the click-through rate for advertisements.
Click-through rates were important proxies for apps in the early days of the internet. And customer acquisition cost might be less important for AI firms than repeat usage (lifetime value).
Model owners and suppliers of computing as-a-service will measure volume of transactions and data processed.
But key metrics may evolve. In the early days of the internet, user engagement and traffic seemed more important. In 1995, for example, page views, unique visitors, time spent on site and click-through rate were arguably the most important.
With greater maturity and actual profits, standard metrics such as revenue growth, profitability, customer acquisition cost or customer lifetime value became relevant.
In its mature phase, active users and user engagement seem more important. So is loyalty, as often measured using the net promoter score.
Internet, 1995 | AI (2023) |
Website Traffic: Measured the number of unique visitors to a website. Indicated the popularity and reach of the website and its potential to attract users and advertisers. | User Engagement: Measures the level of interaction and involvement of users with an AI-powered product or service. Indicated the effectiveness of the AI in providing value to users and its potential for long-term adoption. |
Page Views: Measured the number of times a page on a website was viewed. Indicated the depth of user engagement and the potential for advertising revenue. | Data Volume and Processing: Measures the amount of data processed by an AI system. Indicated the system's ability to handle large amounts of data and its potential for generating insights and value. |
Click-Through Rate (CTR): Measured the percentage of users who clicked on an advertisement on a website. Indicated the effectiveness of the advertisement in attracting user attention and driving clicks. | Accuracy and Precision: Measures the ability of an AI system to produce correct and consistent results. Indicated the system's reliability and its potential for generating valuable insights. |
Unique Visitors: Measured the number of individual users who visited a website. Indicated the reach of the website and its potential to attract a diverse user base. | Revenue Growth: Measures the increase in revenue generated by an AI-powered product or service. Indicated the commercial viability of the AI and its ability to generate financial returns. |
Time Spent on Site: Measured the average amount of time that users spent on a website. Indicated the level of user engagement and the potential for monetization through advertising or subscriptions. | Return on Investment (ROI): Measures the financial return generated by an AI investment. Indicated the value of the AI in driving business outcomes and profitability. |
Customer Acquisition Costs (CAC): Measured the cost of acquiring new customers for an internet-based business. Indicated the efficiency of marketing and sales efforts and the potential for profitability. | Customer Lifetime Value (CLV): Measures the long-term financial value of a customer to an AI-powered business. Indicated the ability of the AI to retain and grow a customer base and generate recurring revenue. |
Brand Awareness: Measured the recognition and perception of a company's brand among potential customers. Indicated the company's ability to attract and retain users in a competitive market. | Industry Recognition: Measures the recognition of an AI company's achievements and innovations within the AI industry. Indicated the company's reputation and potential for leadership in the field. |
Presumably, AI firms will move through similar stages as they mature. Early on, analysts will try to quantify the value of intellectual property, data assets or the danger competitors represent.
In some cases, volume of data processed, accuracy of AI models, or adoption of AI solutions by customers could be proxies for growth. Customer acquisition costs could be another metric when traditional metrics do not yet apply.
Revenue mix or reliance on few or broadly-diversified customers might also matter. But key metrics will change as the AI industry matures. Then the more-traditional financial metrics will apply.
Usage matters now, but engagement will matter later.
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