New Technology Often Requires Inventing New Interim Proxies for Financial Potential

When a new technology such as artificial intelligence creates new kinds of value, the traditional financial metrics (revenue, profit, return on investment) often fail to capture progress in the early years.

Instead, industries invent intermediate operating metrics: proxies that signal whether the new model is working before the business model is fully proven. Sometimes it works; sometimes it doesn't.

Lots of dot-com firms touted "eyeballs" as a measure of attention. Many competitive telecom firms used metrics such as "access line equivalents" (taking total bandwidth and breaking it into voice grade "line" equivalents) as an example of potential revenue upside.

These metrics usually measure one of three things:

• Adoption (how many people use it)

• Engagement or usage intensity

• Network growth or installed base. 

Stage	What Firms Measure
Early technology adoption	Installed base, users, traffic
Network growth	Engagement, interactions, ecosystem size
Monetization phase	Revenue per user, margins
Mature industry	Standard financial metrics

In the computing business, there are many examples. 

Technology Wave	Era	Early Operating Metric	What It Measured	Later Financial Metric That Replaced It	Firms
Personal computers	1980s	Installed PC base	Growth of computing platform	Software and hardware revenue	Microsoft, Apple
Dial-up Internet	Early 1990s	Subscribers / online accounts	Growth of consumer internet access	ARPU and subscription revenue	America Online
Web portals	Late 1990s	Page views	Traffic volume and advertising potential	Ad revenue per user	Yahoo
Dot-com era websites	1998–2001	“Eyeballs” (unique visitors)	Audience reach	Advertising revenue	Netscape ecosystem sites
Telecom data services	1990s–2000s	Access Line Equivalents (ALEs)	Aggregate network demand	ARPU and service revenue	telecom carriers
Search engines	Early 2000s	Queries per day	Demand for information retrieval	Revenue per search / ad revenue	Google
Social media	2005–2015	Monthly Active Users (MAU)	Network size and engagement	Ad revenue per user	Meta Platforms
Cloud computing	2010s	Compute instances / workloads	Adoption of cloud infrastructure	Revenue growth and margin	Amazon Web Services
SaaS software	2010s	Annual Recurring Revenue (ARR)	Predictable subscription base	Free cash flow and margin	Salesforce
Sharing economy	2010s	Gross bookings / rides	Platform usage volume	Take rate and net revenue	Uber
Streaming video	2010s	Subscribers	Platform scale	ARPU and operating margin	Netflix
Cryptocurrency	2015–2022	Wallets, hash rate, total value locked	Network security and participation	Transaction fees and financial services revenue	Coinbase ecosystem
Generative AI	2023–present	Tokens processed / active developers / API calls	Real workload demand	Revenue per model usage	OpenAI

Many could note a similar pattern for AI. New metrics emerge because we cannot typically measure early impact using traditional financial measures:

• Monetization lags adoption

• Network effects require scale first

• Investors need forward-looking signals, so usage metrics answer that question before profits exist.

Phase	Typical Metric
Technology novelty	Install base
Early growth	Users or traffic
Platform stage	Engagement
Business model maturity	Revenue per user
Mature industry	Profitability

The AI economy therefore creates new metrics in the interim:

• Tokens processed

• Active developers

• Inference workload

• Model training compute. 

These resemble earlier indicators in the early internet era such as:

• page views (web)

• queries (search)

• Monthly active users (social media)

Eventually the industry will likely shift to measures more closely tied directly to firm profits and revenues:

• revenue per AI workload

• enterprise productivity gains

• profit margins on AI services.

Eventually, we’ll learn which operating metrics actually have higher predictive value, and which have less. 

During the dot-com bubble around the turn of the century, some metrics turned out to have near-zero predictive value.

Company	Metric Highlighted	What the Metric Measured	Why It Was Misleading
Pets.com	Website traffic / brand awareness	Consumer interest in online pet supplies	Traffic did not translate into profitable orders because shipping costs exceeded margins
Webvan	Number of cities launched	Geographic expansion of grocery delivery infrastructure	Massive capital spending occurred before proving unit economics
eToys	Revenue growth rate	Rapid expansion of online toy sales	Sales were heavily subsidized by marketing and discounting
TheGlobe.com	Registered users	Size of social community platform	Users were mostly non-paying and generated little revenue
Boo.com	Site engagement and global launch presence	Interest in online fashion retail	Extremely expensive website technology created slow performance and high operating costs
Excite	Page views	Web portal traffic volume	Advertising demand could not support the scale of infrastructure spending
Lycos	Unique visitors	Audience size of web portal	Monetization per visitor was extremely low
Broadcast.com	Streaming traffic and media partnerships	Growth of internet audio/video streaming	Technology and bandwidth costs exceeded realistic revenue models
Priceline (early phase)	Gross travel bookings	Total value of transactions handled	Gross bookings overstated the company’s actual revenue capture
Drkoop.com	Health site visitors	Consumer interest in medical information	Advertising revenue insufficient to support operations

Netflix Versus YouTube an Example of Industry Boundaries Crumbling

I have not in the past viewed YouTube as Netflix’s most important rival, largely because of the distinction between consumer behavior related to short-form and long-form video, much as I once viewed social media and “professional media” as indirect competitors. 

But technology disruption often leads to market disruption and rearrangement. And so it appears we can make the argument that the key competitor for Netflix is not cable TV or Disney or another long-form streaming service, but YouTube, an app we all have long associated with user-generated content. 

This can happen because technology collapses boundaries between roles in a value chain. 

When the cost of performing a function falls dramatically, firms that historically occupied different layers of the stack can, by choice or circumstance, suddenly become competitors.

Technology Change	Industry Boundary That Collapsed	New Competitors That Emerged	Incumbent Industry	Example Firms
Internet search and digital advertising	Media vs. technology platforms	Search engines competing for ad revenue	Newspapers, magazines, TV	Google vs. The New York Times Company
Streaming video distribution	Cable networks vs. software platforms	Streaming services competing with TV networks	Cable and broadcast TV	Netflix vs Comcast
Smartphones and app stores	Device maker vs. software platform vs. media distributor	Phone companies competing for content distribution	Media distribution	Apple vs. Disney
Cloud computing	Enterprise IT vendors vs. infrastructure providers	Hyperscalers competing with enterprise software vendors	Enterprise software	Amazon Web Services v.s Oracle
Ride-sharing apps	Transportation company vs. software platform	App platforms competing with taxi fleets	Taxi industry	Uber vs. traditional taxi operators
E-commerce logistics platforms	Retailer vs. logistics provider	Retail platforms competing with delivery companies	Retail + parcel delivery	Amazon vs. UPS
Digital payments	Banks vs. software companies	Technology firms competing with banks	Retail banking	PayPal vs. JPMorgan Chase
Online travel platforms	Travel agencies vs. software marketplaces	Online platforms competing with hotel distribution	Travel agencies	Booking Holdings vs. hotel chains
Social media platforms	Media publishers vs. social platforms	Platforms competing for audience attention and ad budgets	Media companies	Meta Platforms vs. news publishers
Electric vehicles + software	Automakers vs. software companies	Software companies entering mobility	Auto manufacturers	Tesla vs. legacy automakers
Smart home platforms	Consumer electronics vs. home services	Platforms competing with appliance makers and utilities	Appliance manufacturers	Google (Nest) vs. Honeywell
Generative AI	Software tools vs. knowledge work	AI models competing with software products and service firms	SaaS, consulting, creative services	OpenAI vs. enterprise software vendors

When the cost of performing a function falls dramatically (because of computing, networks, AI, logistics platforms, etc.), firms that historically occupied different layers of the stack can become competitors.

In the case of Netflix and YouTube, there is only so much time available to people, and media consumption is no different. So all media compete with all others for attention. 

In that sense, both YouTube and Netflix are bigger direct competitors in the sense that both compete for the same scarce resources: time on the TV screen, advertiser budgets and cultural attention.

One has to get past the differences of business models and content format.

Recent Nielsen “The Gauge” data shows YouTube as the top streaming app on TV sets in the United States, with around 12 to 13 percent of total TV usage versus roughly eight percent for Netflix. So on the dimension of “time,” they are competitors, if still mostly indirect.

Advertisers see both as desirable venues.

And both are expanding into each other’s turf: Netflix is adding ads, live events and creator‑style formats, while YouTube hosts full‑length movies and TV and invests in higher‑end production and living‑room viewing, blurring the old “user-generated content versus Hollywood” characterization.

YouTube commands enormous daily time spent, with average global usage around 50 minutes per day per user on social platforms, and it leads all streaming apps in total TV watch time in the United States, which directly overlaps with Netflix’s engagement metric of hours viewed.

Short‑form vs long‑form content preferences have in the past been differentiators between Netflix and YouTube (“how each earns its revenue”), but as television and digital video ad budgets shift away from traditional TV, every dollar is now effectively a choice between YouTube, Netflix, and a shrinking set of legacy media apps.

So a greater degree of direct competition in the future seems inevitable, as different as the two providers have been, historically. 

But that also is a good example of how technology disruption leads to market disruption, including the creation of new competitors in established businesses as well as new competition between contestants formerly seen as operating in different parts of the market. 

What "Boomers" Messed Up

Journalist Helen Andrews is not going to be popular with lots of readers of her book Boomers: The Men and Women Who Promised Freedom and Delivered Disaster. But as a U.S.-raised “boomer” myself and a former practicing journalist, I must agree with her general thesis, if not with all of her examples. 

First of all, U.S. boomers have been perhaps-accurately described as extraordinarily self absorbed. Worst, they arguably have been destructive of culture. 

“The boomers destroyed the fabric of society,” says Charles Haywood, a Gen X commentator:

• They demanded rights, and rejected responsibilities

• They destroyed families

• They destroyed education, substituting cant and leftist ideology for rigor and the transmission of American ideals

• They crushed the working class, both through their extractive economics, such as globalization, and by smashing all intermediary institutions, while they exalted the federal government and the priesthood of the administrative state, thereby neutering the working class as a political force and mutating beyond recognition the political structure of the Republic

• They destroyed high culture in everything from architecture to music to film.

“The essence of boomerness, which sometimes manifests itself as hypocrisy and other times just as irony…(is that) they tried to liberate us, and instead of freedom they left behind chaos,” she writes. “They inherited prosperity, social cohesion, and functioning institutions. They passed on debt, inequality, moribund churches, and a broken democracy.”

Strong words, indeed. Some might just say she is “mean,” rather than “telling the truth in love.” I mostly want to know whether what she sees approximates truth. 

“We experimented with our own lives and those of others; we turned out to be mistaken,” says commentator Lloyd Robertson. Indeed, one author calls boomers “sociopathic,” indeed “deceitful, selfish, imprudent, remorseless and hostile.” 

“Only a few decades ago, it might have seemed progressives wanted to replace dogmatism of any kind with a new openness: a true welcoming of different points of view, a marketplace of ideas comparable to the agora in ancient Athens,” he says. “Instead, of course, we are experiencing the tightening and enforcement of new dogmas.”

Of course, it might also be fair to note that intergeneration conflict is perhaps nothing new, and that not every boomer is well off, smug and arrogant. 

 

Mere epithets, in other words, are not helpful. On the other hand, the charge that boomers are robbing our children of their future, looking at our refusal to fix social security or other entitlements, has policy relevance. 

My own criticism is not so much in that area, but in the disingenuous sense of moral superiority so many boomers seem to embrace. 

 And boomers are not alone. Other generations seemingly have had the same obnoxious self assurance and narrowmindedness (if remaining generally completely unaware of those traits). 

And they seem to have passed on some of those traits to subsequent generations, although some of us have hope that millennials can “break free” from the influence of the 1960s and stop believing that “narcissism is the highest form of patriotism.” 

The broad story is one of individualism substituted for collective good; “me” instead of “we;” “I” instead of “us;” the immediate in place of the enduring. It isn’t pretty, or praiseworthy.