Will AI Disrupt Non-Tangible Products and Industries as Much as the Internet Did?

Most digital and non-tangible product markets were disrupted by the internet, and might be further disrupted by artificial intelligence as well. Non-tangible products are goods or services that cannot be physically touched or held.  

These products  provide value through experiences, expertise, or access, rather than a physical object. Services including legal advice, consulting, haircuts, car washes, travel experiences provide examples. 

So do content products such as e-books, software, online courses, music downloads and video games.

Intellectual property such as patents, trademarks, copyrights, as well as financial Instruments such as stocks, bonds or insurance policies, are examples of intangible products. 

For many of us, internet access and data processing, though supported by very-real tangible platforms, might also be considered intangible products. One uses internet access, but the service is intangible. One uses platforms to process data, but those physical platforms are not the product. Rather, insights, perspectives, discussions, communications and documentation are common outputs and the “products” of the platforms. 

Business models for intangible products have been reshaped by the internet, and stand to be disrupted by AI as well, though the mechanisms might differ. 

In part, the internet disrupted value chains by attacking distribution costs and methods. AI is more likely to disrupt non-tangible product value by altering content production costs and methods. 

But digital technology--and AI--reshape the ways non-tangible products are produced, distributed and consumed.

When analog products are transformed into digital products, they can be replicated and distributed at minimal cost. So scalability grows dramatically, explaining why Netflix can operate globally in a way that legacy media content companies have found difficult. 

New distribution platforms also are possible, as online marketplaces connect creators with customers directly and globally, with fulfillment often possible on-demand. 

Marketing also shifts to online and targeted vehicles, though true for tangible and intangible services, with greater importance on customer experience issues.  

The overall impact of internet mechanisms has been to put pressure on non-tangible product business models, as competition is easier. AI should have many of the same effects.  

Of course, many intangible products have both minimal marginal costs (the cost of producing one additional unit) but also high sunk costs. Connectivity networks, water and electrical networks provide examples. Other networks--such as transportation networks--might also have similar characteristics: high sunk costs to produce the first unit, but low to relatively-low marginal costs for supplying additional units. 

That might suggest the ability to use marginal cost or forward pricing, both of which account for volume or network effects.

Marginal cost pricing sets the price equal to the marginal cost. For most digital goods, this translates to near-zero pricing, as replicating and distributing the product incurs minimal extra expense. But recovery of the sunk costs means that, in practice, marginal cost pricing is rare, even for non-tangible products. 

Forward pricing uses the concept of setting current prices with a view to future expected production costs, as when scale effects occur. 

Traditional pricing models often focus primarily on current production costs (materials, labor) to determine the initial price. Forward pricing takes a longer-term view, factoring in the expectation that production costs will likely decrease as the technology scales up (more units are produced).

Another possible related concept is near-zero pricing, where digital products can take advantage of Moore’s Law impact on the cost of digital infrastructure (computation, memory, bandwidth), and therefore the cost of producing and distributing digital products. 

Near-Zero Pricing: This strategy sets a very low price, often free, to attract a large user base. Revenue can then be generated through advertising, in-app purchases, or freemium models (free basic version with premium features for a fee). Near-zero pricing works best for products with network effects, where value increases with more users (e.g., social media platforms).

Moore's Law Explains a Lot

We will likely never know for certain how much an understanding of Moore’s Law has played a vital role in the fortunes of firms whose business models rely on internet access, but there are tantalizing examples. 

At a time when Netflix was still mailing out DVDs to its customers, internet access was still generally running at about 56 kbps, not fast enough to support video streaming. 

The problem, says Hastings in an interview today at the Wired business conference, was that back then they couldn’t stream movies over 56 kbps modems.

But there was Moore’s Law and improvements in bandwidth which could be plotted, and that is exactly what Hastings did. “We took out our spreadsheets and we figured we’d get 14 megabits per second to the home by 2012, which turns out is about what we will get.”

And Hastings arguably is not the only person whose knowledge of Moore's Law has led to surprising business conclusions. 

Perhaps the most-startling strategic assumption ever made by Bill Gates was his belief that horrendously-expensive computing hardware would eventually be so low cost that he could build his own business on software for ubiquitous devices. 

How startling was the assumption? Consider that, In constant dollar terms, the computing power of an Apple iPad 2, when Microsoft was founded in 1975, would have cost between US$100 million and $10 billion.

Optical fiber communications in the local loop does progress, in terms of bandwidth, about as fast as Moore's Law, even if the progress of optical fiber in the local access network does not necessarily progress at that rate. 

In other words, Hastings and his team understood there would come a moment when video streaming was feasible, based in large part on internet access trends propelled by Moore’s Law improvements in semiconductor technology. 

A perhaps-related insight might be inferred. Moore’s Law contributes to a trend of ever-lower costs for computation and communications. 

Over time, what that means, as a practical matter, is that applications can be created, and use cases created, that assume the cost of computing and communications is no barrier to widespread use. Some of us might point to the development of high-definition TV as an example. 

At a time when analog versions of HDTV required 40 Mbps per channel, some believed HDTV could be done in six Mbps per channel. As ait turns out, we can do so using less bandwidth than that. 

We might argue that a wide range of businesses, use cases and applications now are possible precisely because of Moore’s Law impact on the costs of computation and communication, ranging from financial technology including mobile payments to fraud detection; cloud computing; social media; e-commerce; the sharing economy; affordable artificial intelligence or the internet of things. 

Navigation apps; all forms of on-demand services; video streaming and every form of recommendation and personalization features, plus speed-to-text or text-to-speech are enabled by radically-lower costs of computation.

"It's Different This Time" is Among the Greatest Dangers in Financial Markets; Sometimes in Software and Computing

“It’s different this time” is a classic example of the sort of thinking that can underpin financial bubbles. The phrase encapsulates the belief that "misconceptions" about a few “laws of economics” exist.

One might argue the same sort of argument has been made in the information technology business from time to time. In the capacity business, there was the Enron Broadband effort to create commodity exchanges for data transport.

Critics of the model were sometimes told "you don't get it." As it turns out, skeptics were right. In the IT business, a related sort of argument sometimes arises. You might hear talk of "paradigm shifts" or perhaps allusions to technological "singularity."

In the chip world, something analogous is the argument that "Moore's Law is dead."

Perhaps there is no clear harm in the expression of the ideas. But there are clear business consequences if leaders decide "it really is different this time."

Some might argue the classic relationship between interest rates and recessions has fundamentally changed. “It’s different this time” is the argument. That might imply we do not have to worry about recessions caused by high interest rate policy.

Others might argue the historic relationships between unemployment and inflation are “different this time.” That might imply we do not need to worry about employment rates and inflation.

Consider the argument that the relationship between interest rates and recessions (higher rates lead to recessions; lower rates promote growth) has fundamentally changed. Some now argue traditional tools such as rate hikes are less effective in combating inflation or preventing recessions.

Increased government intervention and changes in central bank mandates such as a focus on full employment alongside inflation control might reduce the effectiveness of interest rate policy. 

The argument is that increased activism by central banks (quantitative easing and forward guidance as examples) has arguably altered the relationship between interest rates and economic outcomes. 

Central bank interventions might  influence economic activity and asset prices through channels beyond just interest rates.

Some economists argue that the increasing importance of financial markets has weakened the traditional relationship between interest rates and recessions. Higher interest rates might not dampen demand for loans as effectively because alternative financial instruments exist.

Others suggest that asset bubbles can inflate and burst independent of interest rate changes, potentially triggering recessions, no matter what interest rate policy might be. 

Yet others might argue that globalization might make economies more sensitive to external shocks, automation could dampen demand for labor, and aging populations could reduce aggregate consumption, all potentially operating alongside interest rate policy. 

The traditional argument is that interest rates influence aggregate demand, investment, and therefore economic cycles, and that the relationship remains intact. 

Others might argue that such “it’s different this time” thinking is associated with asset bubbles, and the fundamental relationships between supply and demand still are intact.

Likewise, we might remember to be cautious whenever we hear that a paradigm shift is happening (it might not be happening) or that a singularity is nearing or chip progress rates must decline. Altering business strategy based on incorrect assumptions can be deadly.

Home Broadband Improvements Now Come at Twice Moore's Law Rates

In one sense, we should not be so worried that internet access providers will be able to keep increasing capacity to match end user demand. Consider the rates of improvement for computing and storage, using Moore’s Law, rates of improvement for fixed network internet access and mobile internet access. 

The computing baseline would be a doubling of capability every two years. Using that standard, communication networks lagged Moore’s Law rates of improvement in the 1980s. Fixed networks reached parity in the decade of the 2000s, while mobile networks reached parity in the 2020s. 

Decade	Moore's Law (doubling time)	Home Broadband Speed (doubling time)	Mobile Internet Speed (doubling time)
1980s	2 years	4 years	10 years
1990s	2 years	3 years	6 years
2000s	2 years	2 years	4 years
2010s	2 years	1.5 years	3 years
2020s	2 years	1 year	2 years

In fact, the rate of fixed network capacity increases now exceeds that of Moore’s Law. In other words, fixed network capacities are  improving twice as fast as computing capabilities. And while mobile networks have generally been more bandwidth challenged than the fixed networks, mobile capacity gains now equal Moore’s Law rates of improvement. 

Over time, that results in exponential rates of change. Home broadband speeds, in fact, do correspond with Edholn's Law or Nielsen's Law\ of bandwidth increase. 

Edholm’s Law states that internet access bandwidth at the top end increases at about the same rate as Moore’s Law suggests computing power will increase. Nielsen's Law essentially is the same as Edholm’s Law, predicting an increase in the headline speed of about 50 percent per year. 

Nielsen's Law, like Edholm’s Law, suggests a headline speed of 10 Gbps will be commercially available by about 2025, so the commercial offering of 2-Gbps and 5-Gbps is right on the path to 10 Gbps. 

source: NCTA  

To be sure, some access providers worry about capital investment costs. But, historically, internet service provider revenues and higher speeds have moved largely in tandem, even if cost per bit metrics show a steady trend towards lower per-unit cost. 

The overall trend for internet access is higher consumption at lower unit costs. In other words, we use more data, consumed at higher speeds, but also at more-affordable costs over time, adjusting for inflation and hedonic improvements. 

It is hard to answer the question “have home broadband prices risen since 2009?” without using hedonic adjustment and also adjusting for inflation. The Bureau of Labor Statistics uses hedonic adjustment to track producer prices for home broadband, for example, since speed and other attributes change over time. 

The rationale is that a dial-up internet connection is not a  comparable service to home broadband at various speeds (10 Mbps, 100 Mbps, 1 Gbps, for example). Since prices tend to stay about the same over time while speeds have increased for the “most bought” tiers of service, BLS adjusts prices to account for quality improvements. 

source: Bureau of Labor Statistics 

The bottom line is that ISPs do not seem to be faced with business scenarios where consumer demand cannot be supplied.

Non-Linear Development and Even Near-Zero Pricing are Normal for Chip-Based Products

It is clear enough that Moore’s Law played a foundational role in the founding of Netflix, indirectly led to Microsoft and underpins the development of all things related to use of the internet and its lead applications. 

All consumer electronics, including smartphones, automotive features, GPS, location services; all leading apps, including  social media, search, shopping, video and audio entertainment; cloud computing, artificial intelligence and the internet of things are built on the foundation of ever-more-capable and cheaper computing, communications and storage costs. 

For connectivity service providers, the implications are similar to the questions others have asked. Reed Hastings asked whether enough home broadband speed would exist, and when, to allow Netflix to build a video streaming business. 

Microsoft essentially asked itself whether dramatically-lower hardware costs would create a new software business that did not formerly exist. 

In each case, the question is what business is possible if a key constraint is removed. For software, assume hardware is nearly free, or so affordable it poses no barrier to software use. For applications or computing instances, remove the cost of wide area network connections. For artificial intelligence, remove the cost of computing cycles.

In almost every case, Moore’s Law removes barriers to commercial use of technology and different business models. The fact that we now use millimeter wave radio spectrum to support 5G is precisely because cheap signal processing allows us to do so. We could not previously make use of radio signals that dropped to almost nothing after traveling less than a hundred feet. 

Reed Hastings, Netflix founder, based the viability of video streaming on Moore’s Law. At a time when dial-up modems were running at 56 kbps, Hastings extrapolated from Moore's Law to understand where bandwidth would be in the future, not where it was “right now.”

“We took out our spreadsheets and we figured we’d get 14 megabits per second to the home by 2012, which turns out is about what we will get,” says Reed Hastings, Netflix CEO. “If you drag it out to 2021, we will all have a gigabit to the home." So far, internet access speeds have increased at just about those rates.

The point is that Moore’s Law enabled a product and a business model  that was not possible earlier, simply because computation and communications capabilities had not developed. 

Likewise, Microsoft was founded with an indirect reliance on what Moore’s Law meant for computing power. 

“As early as 1971, Paul (Allen) and I had talked about the microprocessor,” Bill Gates said in a 1993 interview for the Smithsonian Institution, in terms of what it would mean for the cost of computing. "Oh, exponential phenomena are pretty rare, pretty dramatic,” Gates recalls saying. 

“Are you serious about this? Because this means, in effect, we can think of computing as free," Gates recalled. 

That would have been an otherwise ludicrous assumption upon which to build a business. Back in 1970 a “computer” would have cost millions of dollars. 

source: AEI 

The original insight for Microsoft was essentially the answer to the question "What if computing were free?". Recall that Micro-Soft (later changed to MicroSoft before becoming today’s Microsoft) was founded in 1975, not long after Gates apparently began to ponder the question. 

Whether that was a formal acknowledgement about Moore’s Law or not is a question I’ve never been able to firmly pin down, but the salient point is that the microprocessor meant “personal” computing and computers were possible. 

A computer “in every house” meant appliances costing not millions of dollars but only thousands. So three orders of magnitude price improvements were required, in less than half a decade to a decade. 

“Paul had talked about the microprocessor and where that would go and so we had formulated this idea that everybody would have kind of a computer as a tool somehow,” said Gates.

Exponential change dramatically extends the possible pace of development of any technology trend. 

Each deployed use case, capability or function creates a greater surface for additional innovations. Futurist Ray Kurzweil called this the law of accelerating returns. Rates of change are not linear because positive feedback loops exist.

source: Ray Kurzweil  

Each innovation leads to further innovations and the cumulative effect is exponential. 

Think about ecosystems and network effects. Each new applied innovation becomes a new participant in an ecosystem. And as the number of participants grows, so do the possible interconnections between the discrete nodes.  

source: Linked Stars Blog 

 

So network effects underpin the difference in growth rates or cost reduction we tend to see in technology products over time, and make linear projections unreliable.

Which Came First: the Chicken or the Egg?

Technological determinism--the idea that technology shapes culture, society and history, independent of politics, economics, religion or other cultural forces--might mistakenly be applied in the connectivity and computing businesses. 

In other words, some might think we have the internet because of personal computers, because of Moore’s Law, because of TCP/IP, digital media, smartphones and tablets. One might think we have e-commerce, affordable video conferencing or social media because of broadband internet access, cloud computing or fiber to the home. 

That arguably gets it backwards, as we similarly contend that ubiquitous and high-quality broadband access creates economic growth. The causal relationship is likely the other way: wealth and high rates of economic growth create the demand for quality broadband access, use of smartphones, e-commerce and social media. 

Telco network architectures have undergone significant changes since 1970, looking at the basic switching function. But technology change does not seem to explain the revenue and business model shifts that have transformed the industry. Architecture, for example, does not explain the shift to mobile revenues and the decline of fixed network revenue.

In 2023, in most countries, mobile service represents 70 percent to 80 percent of total service provider revenues. At Verizon, for example, mobility represents no less than 55 percent of total revenues while consumer revenues drive 77 percent of revenue.  

source: STL Partners 

Fixed network revenue as reported to the U.S. Federal Communications Commission and based on revenues contributing to the universal service fund. It is skewed downward because mobile service revenues do not contribute to USF at the same rate as do fixed network services. 

Instead, demand changes--accelerated by the introduction of competition; the value of mobility and the emergence of the internet--seem to explain the key business model changes. Architectural and physical media and switching evolutions seem less important drivers of business model change. 

In the 1970s, for example, telco revenues were dominated by voice services, with the bulk of profit coming from international and long distance calling by business customers.

That began to change a bit in the 1980s, as the conversion to digital switching and Signaling System 7 enabled touch tone dialing, call waiting, call forwarding and voice mail. Some incremental revenues also were generated by business customers using Integrated Services Digital Network. 

Business applications such as video conferencing, remote access, and digital phone systems generated new revenue based on use of ISDN. 

But the bigger change was the shift of revenue from fixed network services to mobile services in the later 1990s and 2000s. 

source: IDATE 

The emergence of demand for internet apps and services also displaced voice as the key driver of fixed network revenues. 

At least some connectivity providers also added significant revenues from entertainment video services. Cable TV companies always had done so, but added voice, then internet access and mobility services. 

Telcos added video to their menus of voice and internet access. By the second decade of the 2000s, mobility and broadband internet access had become the number-one and number-two biggest revenue sources for most access service providers. 

Demand changes explain more of the actual revenue shift in the access business since 1970 than the adoption of digital switching or optical fiber access media. 

Mobile services substantially displaced the fixed network as the preferred way people make phone calls and use messaging or social media. That, of course, requires use of new mobile networks, but the networks did not drive demand. Demand requires the networks. 

Likewise, one might argue that the emergence of the internet drives the demand for broadband access. It is worth noting that consumer demand drives both mobility and broadband access revenue. 

Historically, technology innovations or demand for “advanced” services came from business users. That was true for long-distance calling, ISDN and business phone systems, for example. 

That pattern held in the early days of mobility, but has reversed. Today, consumer users drive mobility revenue magnitudes. Also, internet access drivers were led, early on, by consumer demand. 

The point is that technology changes more often reflect demand changes than cause them. Demand changes, in turn, have been increasingly driven by new value consumers perceive in mobile phones and the internet, with declining value seen in fixed network voice services. 

Though one can document changes in network architectures, signaling methods or physical media changes, business model changes have happened for non-network reasons: competition, the value of the internet and mobility.