While the jury is out on the extent of impact GenAI and vibe coding will have on building mission-critical enterprise products…
Here are some thoughts on how AI can help turn the product flywheel:
- Use GenAI to enable fuller-stack engineers and reduce tech debt
- This will enable you to organize smaller product/outcome oriented teams
- These teams can achieve more with fewer dependencies and streamlined processes (even without looking at opportunities to streamline product dev processes themselves by using AI)
- GenAI can enable cheaper, faster experimentation / discovery (it compresses the truth curve by reducing the cost of pretotyping style product experimentation techniques)
- Cheaper experimentation allows for more “shots” for the same innovation capacity.
- Which leads to faster product market fit, more likely product/feature fit
- As well as reduced toil related to “failure demand” (customer who misunderstand the product, product failures, etc.)
- Freeing even more capacity to discover/explore/grow/reduce technical debt / improve the architecture
As this flywheel turns faster and faster, the product organization delivers better and better products and outcomes in an increasingly sustainable and resilient manner, with product organization operating systems that become simpler and more streamlined over time, rather than more complex.
“Not everyone can do this… New companies, sure. Larger, established companies are knee-deep in mountains of code, dependencies, and tech debt. Whenever you need to build an MVP that depends on your existing product, good luck…”
Yes, the jury is still out on how helpful “vibe coding” and GenAI code can be for large-scale brownfield/legacy software products in general.
The interesting opportunity I’m highlighting here is that GenAI, in general, and vibe coding, specifically, can make it easier to experiment in the pre-MVP stage, also referred to as Pretotyping.
Some of these experiments don’t even require coding. But a product team could run a landing page, a fake feature, an explainer video, and other prototyping techniques much faster using GenAI as an accelerator. (This is what we cover in our Professional Product Discovery workshops)
When you have built enough confidence in the truth curve that it makes sense to build an MVP, vibe coding should be considered as an approach to create a real, throwaway experiment intended to validate or invalidate.
Indeed, there will be some environments where the only way to conduct meaningful experiments will be to build something that integrates with your existing system or product; in such cases, vibe coding might not be beneficial.
In these cases, it may be beneficial to allocate more time to building higher conviction through pre-MVP discovery and experimentation techniques, before proceeding to expensive development in your real product code.
Yes, new product development in a greenfield is easier in many ways.
The advantage that product teams with existing products and customers have is precisely that – they have these customers. It should be easier for them to have conversations with these customers about problems, opportunities, needs, and jobs they’re trying to get done.
You win some, you lose some…
What are some additional ways you’re leveraging GenAI to help turn the Product flywheel?
Have you used GenAI as an early pre-MVP experimentation technique? I’d love to hear about your experience…
This article was originally published on the GenAI section of Yuval's Scaling w/ Agility Newsletter.