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?
