One remarkable thing about the invention of generative AI is that it’s one of the first examples of a probabilistic computer that produces outputs that are varied and non-deterministic. Some, who expect these systems to behave like traditional systems, have complained about hallucinations—however, these complaints miss the point. A dispersion of outputs (including hallucinations) is exactly what’s so special here. In fact, they unlock a whole new category of product design: probabilistic products.
By probabilistic products, we mean products with non-deterministic and often emergent attributes. Social networks are an example of probabilistic products. The emergent behaviors that appear on these networks typically cannot be predicted but, instead, can simply be observed. One way to think about this is to organize product categories, or a product’s use cases, into three groups:
- Those that are uniquely enabled by or benefit from the probabilistic nature of the platform
- Those that are tolerant of the probabilistic nature of the platform
- Those that are intolerant of the probabilistic nature of the platform
This first group includes popular image models like Midjourney and Stable Diffusion, whose magic comes from the wide variety of outputs they generate. This entire category of generative media is uniquely enabled and able to produce stunning results because the platforms are non-deterministic. The AI companionship category also fits within this grouping, as the whole value of the product is the very human-like lack of predictability in the interactions; the loss of non-deterministic outputs would cripple the product experience. Another example in this space could include assisted shopping, as an AI’s unexpected and unusual clothing recommendations are exactly what can help shape one’s style and taste.
A second group of products may tolerate probabilistic outputs but don’t necessarily benefit from the non-deterministic nature of the platform. For example, think about products that synthesize existing content, where variability in how the synthesis is presented is generally fine so long as the gist of the synthesis is accurate. Notably, synthesizing numeric content (e.g., your stock portfolio performance) requires a higher degree of accuracy, while the synthesis of written content can have far more variance without sacrificing value. Even code generation is tolerant of variance so long as the code functions as expected.
Finally, there are a large number of use cases that demand deterministic outputs. These examples include everything from financial projections to tax calculations to driving directions—essentially any field that requires a definite answer to set inputs.
One can also think about individual products as being subdivided into a product architecture that’s organized around tolerance to probabilistic outputs.
For example, picture an AI assistant whose purpose is to help you file your taxes. Such an assistant could be organized into three subsystems that have different expectations of the underlying platform. First would be a human interface that is uniquely powered by a probabilistic LLM. Next, the synthesis of context could be tolerant of probabilistic outputs. Finally, the actual tax calculations could (and should) be done on a traditional engine that’s outside the bounds of an LLM, thus guaranteeing the accuracy of the outputs.
In sum, a new computing architecture demands a new product architecture, and the probabilistic nature of LLMs and GenAI pipelines as a platform creates unique opportunities—and demands—that products and use cases be carefully considered in their tolerance to a dispersion of outputs. Product pickers and designers will do well to consider this carefully in designing experiences that leverage these new capabilities.
For more of my thoughts on why now is the time for product pickers and engineering-oriented founders to succeed in generative AI, read my piece on aligning founder superpowers with product cycles.
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