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?