Will AI Have Impact More Like the PC or the Internet? Why it Matters

One reason it is conceptually hard to imagine the impact of artificial intelligence is that it is likely to have business impact along the same lines as did Moore’s Law or the internet: removing key cost barriers and enabling new business models. 

And though some outcomes are easy to envision, such as automating functions or removing geographic barriers, others are hard to grasp because they simply did not exist before. Search and social media are examples. 

In other words, as Moore’s Law led to the elimination of key constraints regarding the cost of computing and software, while the internet created new possibilities for product distribution and sales,, AI might well eliminate key barriers in a value chain.

That will allow lots of industries to evolve in ways that were not possible before, and possibly also create a few new industries that had not existed previously, as the search and social media businesses emerged with completely-new business models (ad supported technology and user-generated content). 

The way to think about it is to ask, in the context of any business, process or industry, what could be different if the key cost constraint, or a major cost constraint, were reduced to a point where it no longer was a constraint or barrier. .

In other words, the question is something like “what would my business look like if a key input were nearly free?” 

Perhaps the best example is Netflix. It is not entirely clear whether Netflix founder Reed Hastings initially and “always” thought the company would evolve into a video streaming service, but it is clear that he did believe a “deliver your DVDs by mail” service was viable in 1997. 

According to Barry McCarthy (Netflix's CFO from 1999 to 2010) and Neil Hunt (Netflix's Chief Product Officer from 1999 to 2017), they were at a 2005 dinner with Reed Hastings where they sketched out projections of bandwidth costs and speeds on a napkin. They plotted Moore's Law-like curves showing:

• Internet speeds would keep increasing

• Video compression technology would improve

• The cost of bandwidth would continue falling

The key insight from their napkin math was that these trends would intersect at a point where streaming video would become economically viable for a mass market service. Netflix launched video streaming in 2007. 

So think of the ways AI might eventually remove key cost constraints in many industries, as the internet eliminated barriers in retailing.

Retailer Cost Constraint	Traditional Retail	Internet Retail
Inventory Costs	High costs associated with maintaining physical inventory, including storage, handling, and obsolescence	Reduced inventory needs due to drop-shipping models and virtual warehouses, leading to lower storage and handling costs
Real Estate Costs	High costs for physical store locations, including rent, utilities, and maintenance	Lower costs associated with online stores, as they require minimal physical space
Distribution Costs	High costs for shipping and transportation of products to physical stores	Lower costs for shipping directly to customers, especially for digital products
Marketing Costs	High costs for traditional advertising methods, such as print, television, and radio	Lower costs for online marketing, including search engine optimization, social media, and email marketing
Customer Service Costs	High costs for in-store customer service, including staffing and training	Lower costs for online customer service, often automated or outsourced

And we can note many similar constraint removals in other industries, including the creation of entirely-new business and revenue models for search and social media. Both search and social media were examples of “advertising-supported technology” models, something that had not been conceivable or possible before. 

But the internet also enabled a rearrangement of business models in most industries, often focused heavily on distribution methods. 

Industry	Traditional Cost Barriers	Internet Solutions
Retail	High overhead costs (rent, utilities), inventory management, distribution	E-commerce platforms, drop-shipping, digital products
Media	Printing costs, distribution logistics, limited reach	Online publishing, streaming services, social media
Software	Physical distribution, licensing costs	Digital distribution, SaaS models, open-source software
Education	Infrastructure costs, geographical limitations	Online courses, MOOCs, virtual classrooms
Finance	Branch network costs, transaction fees	Online banking, mobile payments, cryptocurrency
Travel	Agency fees, booking limitations	Online travel agencies, direct bookings, peer-to-peer platforms
Entertainment	Production costs, distribution channels	Digital content creation, streaming platforms, social media
Manufacturing	Supply chain costs, inventory management	3D printing, on-demand manufacturing, global sourcing
Customer Service	Infrastructure costs, geographical limitations	Online help desks, chatbots, AI-powered support
Professional Services	Geographical limitations, overhead costs	Remote work, online collaboration tools, freelance platforms

Consider the importance of Moore’s Law for the software industry’s “forward pricing” of its products.

Forward pricing is a strategy of setting prices for current products based on anticipated future costs and market conditions, rather than just current costs. 

Microsoft in the 1980s and 1990s, for example, is said to have deliberately released new products that both required more-powerful hardware and also with the expectation that the hardware would catch up. 

In the gaming Industry, products often were designed around advanced hardware that had not yet become mainstream, assuming that would happen and that costs for the platforms would drop. 

Suppliers of enterprise software arguably made the same assumptions, building features that required better hardware and platform upgrades.

On the other hand, initial high prices were expected to fall rapidly, creating the potential for mass market adoption though initially focusing on early adopters. 

The key issue at the moment is that it is very hard to conceive of entirely new ways an existing industry can innovate using AI, to revamp its value chains. It arguably is even harder to envision the emergence of at least a few entirely-new industries that do not presently exist. 

The personal computer and the internet have enabled the emergence of entirely industries or industry segments. For example, the independent software industry was enabled by the PC, along with lots of “PC-specific” industry functions. 

The internet arguably has had more-profound impact, enabling e-commerce, social media, search, cloud computing, digital advertising and streaming media. 

Personal Computer	Internet
PC Manufacturing	E-commerce
Operating Systems	Social Media
PC Software	Cloud Computing
Computer Peripherals	Digital Advertising
PC Gaming	Streaming Media
Desktop Publishing	Online Education
Computer-Aided Design (CAD)	Cybersecurity
PC Repair Services	Web Hosting
PC Retail	Search Engines
PC Magazines/Media	Digital Payment Systems

That should raise questions about the potential AI impact: will it mostly create new industry sub-sectors that support the use of AI itself, as did much of the PC ecosystem, or will it transform whole functions and industries, as arguably was the case for the internet?

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.

Does Bandwidth "Want to be Free?"

About 25 years ago there was significant discussion in industry circles about the implications of essentially free bandwidth, computing and storage. Bandwidth providers were outraged by the suggestion, as you might guess. 

Around the turn of the century, Bill Gates irritated executives in the communications ecosystem by arguing that “bandwidth wants to be free? ” Others at the time quipped about whether “computing wants to be free?” Others might argue that data wants to be free. And some have been arguing that content wants to be free. 

Twenty years later, we are tempted to argue that Gates was more right than wrong, both about computing and bandwidth. 

To be sure, Gates did not mean computing or bandwidth would literally “cost nothing.” He only meant that neither computation nor bandwidth would not be a constraint to creating new services and apps. 

In 2004, Gates argued that “10 years out, in terms of actual hardware costs you can almost think of hardware as being free — I’m not saying it will be absolutely free — but in terms of the power of the servers, the power of the network will not be a limiting factor,” Gates said.

You might argue that is a position Gates adopted recently. Others would argue that has been foundational in his thinking since Micro-soft was a tiny company based in Albuquerque, New Mexico in 1975.

Young Bill Gates reportedly asked himself what his business would look like if hardware were free, an astounding assumption at the time. Keep in mind the audacious assumption Gates made. In 1970 a computer cost about $4.6 million each. 

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

source: AEI 

In 1982 Gates did not seem to go out of his way to argue that hardware would be free, but he did argue it would be cheaper and far less interesting than software. 

 Gates made the argument in 1994. Gates was still saying it in 2004.  

The point is that the assumption by Gates that computing operations would be so cheap was an astounding leap. But my guess is that Gates understood Moore’s Law in a way that the rest of us did not.

Reed Hastings, Netflix founder, apparently made a similar decision. For Bill Gates, the insight that free computing would be a reality meant he should build his business on software used by computers.

Reed Hastings came to the same conclusion as he looked at bandwidth trends in terms both of capacity and prices. 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

How many business models, products and services now are routine and feasible because Moore’s Law keeps driving higher performance and lower cost? How many applications are possible because bandwidth keeps growing in a similar manner? 

Video streaming, early virtual reality and augmented reality, ridesharing, advanced smartphone features, use of millimeter wave spectrum for 5G and all forms of applied artificial intelligence for search, e-commerce and customer service are feasible because computing and bandwidth performance increase while costs are contained. 

Think about the application of computing over time, in situations where business models formerly unthinkable can become quite practical because the cost of computation and storage have become so cheap. 

The key insight is to ask “what would my business look like?” if communications, bandwidth, computing, storage or information or any other scarce or costly input were so available and low cost that those ceased to be constraints to a revenue model. 

The question might also be asked the other way: what does your business look like if a key input becomes too expensive? The key inputs could be labor, knowledge, a raw material, a logistics or supply chain change. 

A related question is “what does my business look like if demand changes in a major way?”

Google, Netflix, Amazon, Apple, Facebook, Square and many other examples illustrate what is possible when computing, communications, devices, transactions and information suddenly cease to be barriers.

But Gates was substantially correct. How many these days would argue against the notion that most public Wi-Fi access is substantially free?

“You can’t use today’s technology constraints to predict tomorrow’s developments,” says Amadeus Consulting CTO John Basso. That fundamental insight, based in large part on Moore’s Law, might once again be more important than often is believed.

You could argue whole businesses now are built on the assumption that technology (especially hardware) constraints disappear over time. All cloud-based apps are built on such assumptions.

Anything we see in consumer internet applications--where capabilities are supplied at no cost to users--provides an excellent illustration. The classic question is what does your business look like if a key cost constraint is removed. 

Though we might have mischaracterized key elements of the argument, ride sharing did raise questions about what it would mean if “cars were free.” They obviously are not “free,” but personal transportation based in part on ride sharing does in some cases affect the case for car ownership. 

The important part of the question is imagining whether a business or product can exist, and what it looks like, if a key cost constraint is removed. 

There is almost never a physical world ability to create Moore’s Law rates of change that are possible in the computing world. But there are going to be many other opportunities in the spaces where computing can alter cost profiles. Think e-commerce in general, ridesharing, lodging apps, video and audio content streaming, videoconferencing, use of millimeter wave spectrum that in an analog technology world is not commercially usable for home broadband. 

But it is hard and unusual to ask the right question: what does my business look like if a key cost input is removed?