A prototype is a product or service’s early sample, model, or release. It is built to test a concept or process and serves as a tangible representation of an idea. Some prototypes are primarily commercial, while others are scientific.
The advent of digitization has significantly revolutionized the prototyping process over several decades in both commercial and scientific spheres. This aspect of the digital transformation lowered the costs of finding errors, sped up the iteration and addition of new features into prototypes, improved accuracy and precision, and enhanced the integration of prototyping with other activities, such as manufacturing.
Despite the progress, confusion persists about the characteristics distinguishing commercial prototypes from their scientific counterparts. That confusion grows when a firm resides in a setting where both types of prototyping occur, such as generative AI or disease detection from medical images. Let’s dig deeper to understand why changing economic conditions blurred some lines.
Commercial
Commercial prototypes play a pivotal role in the innovation process, facilitating the evaluation of a product’s design, functionality, and usability. They also act as a focal point for expanding the application of cutting-edge technologies in new directions, identifying and addressing issues that need resolution before product launch.
In the later stages of product development, especially in modern software, prototypes aim to understand user experience and usability. Designers often make prototypes with beta testers in mind. They want to learn if they correctly anticipated how users will react to new features. If issues arise, the prototype can be adjusted and refined before a design is frozen in a broader release.
Here is an example. One of my former students is involved in a commercial venture to develop and diffuse algorithms that help dentists detect disease in routine X-rays – from cavities to cancer. In addition to everything else they do, the firm makes algorithms for different diseases, trying them in prototypes first. Why? So they can anticipate issues before a dentist ever touches their product.
Commercial prototypes incorporating existing product improvements can serve an additional purpose in some situations, what managers call maintaining a ‘flywheel.’ This ‘flywheel’ is set in motion when a firm develops software that attracts users and learns from users about desirable new features. In an ideal scenario, the prototype tests these new features before a new version’s release. If successful, these features should satisfy existing users and attract new ones, thus generating additional revenue and perpetuating the cycle.
A crucial determinant of the flywheel’s speed is the cost of generating and testing prototypes of new features. Firms would create a gazillion new features as often as possible if every prototype cost nothing. Of course, designers, coders, and managers take time and effort, even with the most agile methods, and the costs add up. So, managers consider options before prioritizing certain features in each prototype.
Maintaining a ‘flywheel’ with the aid of prototypes can involve high-stakes decisions. Prototyping costs and timing are balanced against the reputational costs from error if the prototypes do not deliver on promises and the strategic urgency of releasing a new version. Some mistakes can sink a launch and must be corrected in advance, but so can delays.
In my experience as an observer, assessing the benefits of features is often more complex than forecasting the costs. Firms differ in their soliciting user advice and determining which options to implement. Moreover, environments differ and matter. The specifics of products, the firm’s mission, the competitive setting, and other factors all play a role in determining priorities for prototyping.
Science
As with commercial prototypes, scientific ones represent a functional product or service, and may not embed all the relevant features, nor account for all the interactions among those features. Yet, different concerns shape scientific prototypes. They may confirm or refute a theory or enhance an idea. They may inspire other scientists to replicate the experiment or apply a similar process in a new context, especially if they demonstrate a novel method for testing an idea or concept.
To illustrate, let’s contrast a setting with high and low prototyping costs. Consider prototyping in quantum computing, where the costs are high. Cryptography and possibly other high-precision measurements are so valuable to the US military that, despite large-scale functional and operational products being far off in the future, the US military regards the potential value from experiments as high. Most of those experiments build prototypes, not fully functional products.
Despite the high cost, the military willingly funds research to build prototypes. These test scientific ideas in that area. Relatedly, most of the work occurs on simulations in university and government labs before anything is ever built. Most interestingly, a few specific applications are now within reach. In these areas, some commercial firms are also beginning to prototype.
Contrast that with a setting infused with lower prototyping costs, namely, algorithms for detecting cancer in medical images. Like algorithms for dentistry, the main costs arise from obtaining and cleaning the appropriate images and then tagging them. That takes time. A person (or team) must also train the algorithm.
The prototype costs for disease detection are non-zero but, on average, much lower than in quantum computing. Unsurprisingly, there has been a massive increase in the creation of prototypes for scientific literature.
Like their commercial counterparts, scientific prototypes embed a contrast between the corporal and cerebral. The costs are immediate, while the gains are intellectual. Like the other type, valuing the benefits can be difficult before building a functional product that can operate at large scale, and the representation, while necessarily imperfect, must be designed to shed light on the goals. However, the goals and audience differ, and that makes a world of difference.
Scientific prototypes typically aim to communicate with other scientists with transparent documentation and meticulous detail. This level of detail raises the costs of scientific prototypes, but it is essential. It reflects the scientific community’s commitment to clarity and reliability, which are crucial for reproducibility and scientific knowledge accumulation.
Mixing prototype purposes
The most confusing situations are settings where both scientific and commercial prototypes exist. This happens in commercial areas where science is crucial to advancing products. Designers of one type of prototype may be keenly aware of the other and influence each other. The same team in an organization may even do a single prototype with two purposes: commercial and scientific.
The interplay is most interesting to watch. At their most basic, new scientific prototypes can encourage new commercial ones, and vice versa. The impact of ImageNet and ChatGPT3.5 illustrates how that can happen.
ImageNet is an example where a scientific prototype led to new commercial ones. After the turn of the millennium, Fei-Fei Li hypothesized that the range and size of the training data sets had held back scientific progress in image recognition algorithms. By 2009, she and her team had figured out how to compile tagged training data that achieved a scale of digital images far beyond what anybody had considered. She released ImageNet in 2010.
In 2012, ImageNet enabled convolutional neural networks to demonstrate their superiority convincingly. That had enormous consequences for commercial firms and entrepreneurs, who gained a new understanding of what was feasible and possible.
Here is what made that effort and result so remarkable. No researcher at a firm had thought of doing this, not even researchers in commercial firms with well-funded R&D labs and major search businesses that would directly benefit, such as Google or Microsoft. Yet, though they did not pay a cent for it, those firms benefited from the new understanding. Their commercial prototypes and products changed irreversibly.
Compare that with ChatGPT3.5, introduced in November 2022. In this situation, commercial prototypes have influenced scientific direction.
Before that introduction, OpenAI and other organizations had been making prototypes of large language models for years. In addition to more tokens and a more extensive training set, the interface for 3.5 was new and tailored for use by a non-expert, showcasing improved conversational skills. OpenAI also implemented numerous guardrails for what computer scientists call safety and compliance issues, such as biased and offensive language.
Moreover, when it moved from prototype to product, OpenAI chose what problems to leave unresolved. For example, ChatGPT does not have a low-cost way to measure the incremental contribution of a specific set of training data. That would have been useful because it would help licensing negotiations and contribute to a standardized benchmark that could help anticipate (and perhaps settle) legal copyright claims.
In 2022, the computer science community had not built a prototype with a solution, and neither had OpenAI. After ChatGPT’s success in gaining users, everyone understood its importance. Commercial success has magnified the area’s importance. Scientific prototyping has responded with an explosion of new research on unresolved problems.
In other words, ImageNet’s example illustrates how success in scientific prototyping enabled a new direction for commercial prototyping. OpenAI’s example demonstrates that success in commercial prototyping can focus interest in scientific prototyping on unresolved problems.
These examples also illustrate how important communication is between scientific and commercial prototypes. Yet, there is an essential tension in communications. Scientific activities require transparency, while commercial activities usually remain proprietary and secret, especially if rivals are both prototyping, as is occurring in generative AI. This is why many observers forecast that recent commercial success may interfere with scientific progress in the near term.
Conclusion
Science and commercial prototypes are not assessed using the same criteria and do not try to achieve the same goals. Each needs feedback from different communities, further reinforcing the differences.
When they overlap, commercial interests largely determine the visible direction of prototyping at firms with commercial stakes in a flywheel. However, even though they differ, intelligent commercial firms also engage in scientific prototyping to understand what is feasible and how the underlying technology likely will evolve.
Copyright held by IEEE
October 2024