Ask HN: How do we evaluate real value-add in "wrapper" products?
"Just a ChatGPT wrapper" is becoming a common criticism of AI products, similar to how many successful SaaS products were once dismissed as "just database wrappers", and we can go down the stack.
But what makes the difference between a wrapper and a valuable abstraction? Looking for examples of products that started as "wrappers" but delivered significant value through their abstractions and orchestration.
I guess these are some aspects to consider:
- How important is the underlying technology vs the solution delivery?
- Historical examples of wrapper products that became industry standards
- Evaluation frameworks for distinguishing meaningful solutions from basic wrappers At the core, value isn't about the tech itself; it's about how effectively it solves real customer problems. A truly valuable product is the ability to solve problems. A valuable product strategically uses technology to create smoother user experiences, solve pain points, and deliver measurable results. In the end, tech should be background noise. The real focus is on solving customer problems, not showing off the latest technology. The most valuable products don't just use tech, they make it frictionless and scalable. Not only are SAAS products just database wrappers, but many other products also enhance the capabilities of Postgres and MySQL through the use of a third-party product, such as leveraging DuckDB's analytical capabilities on PostgreSQL. Whether these two different databases can achieve seamless integration remains to be observed. I think it's particularly bad with ChatGPT because of the expensive, black-box nature of it. In some cases you can switch to another LLM, but do you want to take the risk that it gets shut down, the price goes up, performance gets worse?