Epiphany plays an outsized role in the reductionist two-step model of invention. Step one is when an idea pops into an inventor’s head, and step two is when the invention spreads in an economy over time.
This model is misleading in numerous ways that would take a book to enumerate. Today’s column focuses on step two, aiming to one piece. How and why do businesses embed the invention in products and services while the invention spreads?
Cataloging the factors that help businesses embed inventions can yield valuable lessons. Today, we focus on knowledge spillovers, eliminating bottlenecks, and inventing ways to invent.
Spillovers of Knowledge
A spillover is a lesson learned for free or little cost, typically by intelligently watching and imitating somebody else. Importantly, no formal contracting or intellectual property governs how knowledge transfers from one individual to another.
Consider the invention of viral marketing, a low-cost approach to acquiring customers at the outset of the commercial web, as an illustration of spillover’s role. Beginning in mid-1995, several entrepreneurs built a browser-based email system. The idea for viral marketing emerged during a conversation between the founders of Hotmail, an early browser-based email provider, and their VCs, Tim Draper and Steve Jurvetson. By embedding an HTML link in an email, suggested by the VCs, the users performed the marketing on behalf of the email provider every time a user sent an email.
Using that technique, Hotmail acquired millions of users in a short time. More to the point, viral marketing made acquiring customers unexpectedly and incredibly inexpensive. Others watched, recognized why it worked, imitated it, and implemented it in new settings. Due to these spillovers from one experience to another, customer acquisition costs came down for many new commercial services on the World Wide Web.
Here is the point: The spillover made the World Wide Web more valuable and helped the technology diffuse.
Sometimes, knowledge spillovers involve the transfer of broad lessons, not precise details, which, once again, are learned through intelligent observation. The creation of Artist Shops illustrates this. Artist Shops is a service in which the graphic artist authorizes another firm—Threadless in this case—to produce items with prints of artists’ designs—shirts, socks, cups, and whatever.
Artist Shops emerged after Threadless CEO Jake Nickel began experimenting with digital printing in 2012 to bring the new technology to his crowd-sourcing site, which made prints on shirts using screen printing. Though digital printing was of low quality, he (correctly) anticipated that digital printing would soon acquire sufficient quality to substitute for screen printing. His firm mastered digital printing in 2015.
Artist Shops emerged after Nickell realized that his firm could further help the graphic artists with whom it regularly worked in its primary business. They could arrange to fulfill all the products that utilized graphic artists’ designs. This realization was inspired by watching what Shopify, Etsy, and other platforms did and noticing that none of them had tailored their services for graphic artists.
Threadless created early versions of Artists Shops within a year, and graphic artists reacted positively. Today, Artist Shops is one of the most popular services for graphic artists who sell online. Here is the point: As with any knowledge spillover, learning from the prior demonstrations cost Threadless little, and the new inventions of Artist Shops helped digital printing diffuse.
Spillovers of knowledge also happen in research when scientists learn from one another. Scientific inventions build on those spillovers. One well-known example changed artificial intelligence after the millennium. At that point, researchers were experimenting with tagging images but had yet to figure out how to scale the collections beyond a few thousand images.
A young Assistant Professor, Fei-Fei Li, learned from many other efforts and aspired to tag an order of magnitude more. One day, a graduate student told her about a new service called Amazon Mechanical Turk. How about hiring “Turkers” to tag images?
It took a few years, but Turk enabled a viable approach for creating a supply chain to tag millions of images. By 2009, there was enough to start holding a contest, ImageNet. As is well known, the 2012 winner, AlexNet, changed Artificial Intelligence forever.
In all these cases, spillovers alone do not make for advances. At the same time, spillovers help many entrepreneurs with the most challenging step: figuring out how to use a new invention.
Eliminating bottlenecks
Successfully embedding an invention typically involves eliminating a crucial bottleneck. Hugging Face illustrates such an experience.
After years of trying to develop a market for its chatbots, in 2019, an employee created a library of code that implemented Google’s 2017 idea about Transformers. However, in this case, the employee did it for his own model.
As it did with all its efforts, Hugging Face made the library available as open source for anyone to use. Management observed the reaction of other coders, who started building models with the library. Implementing Transformers had been a crucial bottleneck for others. Realizing that others valued the implementation by a Hugging Face employee, management pivoted to building a business around supporting all the activities unlocked by eliminating the bottleneck around implementing transformers.
Today, Hugging Face hosts the models created by users of their library. Virtually every new open-source artificial intelligence (AI) demonstration today calls functions in its library. In other words, their invention of a library of functions and code helped spread transformer technology.
The invention of GitHub CoPilot in 2021 also eliminated a bottleneck. GitHub Copilot is an AI-powered code assistant that raises productivity by helping developers write code by providing real-time suggestions. It leverages machine learning models trained on vast amounts of code.
To appreciate that invention, recall that Microsoft had acquired GitHub a few years earlier and appointed Nat Friedman as CEO and Thomas Dohmke as Chief Product Officer in 2018. Both had landed at Microsoft as acquisition hires in prior years, and they and their team aimed initially to develop the assistant within GitHub’s Integrated Development Environment (IDE).
Due to Microsoft’s earlier one-billion-dollar investment, Friedman, Dohmke, and their team had early access to OpenAI’s models, beginning in the spring of 2021. That motivated some trial and error with different applications. Frustratingly, they had little but their failures to show for it for a few months.
They learned that a crucial bottleneck was in the interface with users. They unlocked the crucial features after they tried the basic outline of architecture familiar to users today – short or long suggestions for code that the user could accept or reject. Quickly, they saw the success among their beta testers and began to release the software more widely, incorporating more tweaks and suggestions by observing more uses.
Today, we call this architecture CoPilot, recognizing its application outside of the GitHub IDE that first spawned it, trained on more than just code. Ever since the software tool has been adding features and improving. Knowledge spillovers have also been significant, as evidenced by many other entrepreneurs who have imitated the basic architecture.
Inventing a Way to Invent
An initial invention can become embedded in a product because a way to perform faster experimentation and iteration has been invented. That matters because many new products undergo iterative changes before launch.
That observation should be evident to anybody who has estimated AI algorithms. Fast iteration is an essential aspect of the new generation of AI algorithms. Hence, the experience at Hugging Face could also illustrate the importance of lowering the costs to iterate, which is what their hub does. Aside from the library of codes, the website further added an area to stage demonstrations of the results. Successful demos attract attention and feedback, which creates experiences that inspire further iteration.
In other words, fast iteration helped AI applications grow and spread. The inventions at Hugging Face helped AI creators invent faster.
The largest online travel agency, Booking.com, provides another illustration of how to think about inventions that facilitate iterative learning. Management invested heavily in an internal infrastructure to conduct low-cost A/B tests on an enormous scale across its platform. Employees are trained to use it and have the discretion to experiment within the infrastructure as much as possible.
Only a minority of experiments succeed. Yet, despite the failure rate, the vast volume of experiments leads to many incremental improvements, which accumulate over time. The firm has gradually and steadily improved over time. Today, it is the largest online travel platform.
In other words, Booking.com’s infrastructure helped its employees experiment faster. The invention of an infrastructure to support iteration enabled another technology to improve a comparatively mature invention: electronic commerce for travel.
Sometimes, the faster learning process takes on a systemic form. A few years ago, I interviewed the team that developed SageMaker for AWS. At the time, the suite of AI applications was aimed at users who did not have the time to learn technical coding languages but wanted something practical.
There was little distance between the users and the SageMaker team, who interviewed the users about their needs and current uses of the AI applications. The suggestions were then implemented, inspiring further application inventions. It is not an exaggeration to say Sage Maker adopted the new technology and set up a system to learn from experience quickly.
In all the above examples, providers anticipated that businesses would invent during the spread of the initial invention. Among other actions, suppliers converse with their users and learn by imitating the inventions of others or providing tools to make inventions easier for their users. No matter what form it takes, all of it helps the technology diffuse.
Conclusion
Unpriced inputs are tempting for innovative purposes because they are inexpensive and focal. Yet, none of this activity—knowledge spillovers, eliminating bottlenecks, or inventing new ways to invent—fits easily into traditional formal R&D processes, nor is it easily measured. The operational efforts may not necessarily appear as a separate line item in an income statement or balance sheet.
That makes it incredibly challenging to manage. How does a firm know how much to invest when there is no ready benchmark for monetary costs and gains? The lack of systematic measurement also reflects the ad hoc nature of many efforts. There are benefits but also dangers. A firm could put itself out of business with its own ingenuity.
Recognizing this pattern, this topic deserves more attention as a driver of productivity growth and economic change.
Copyright held by IEEE
February, 2025