zlacker

>>namany+(OP)
I think one of the interesting things here is that AI doesn't need to be able build B2B SaaS to kill it. So much of the overhead of B2B SaaS companies is thinking about multitenancy, intergrating with many auth providers and mapping those concepts to the program's user system, juggling 100 features when any given customer only needs 10 of them, creating PLG upsell flows to optimize conversions, instrumenting A/B tests etc...

A given company or enterprise does not have to vibe code all this, they just need to make the 10 features with the SLA they actually care about, directly driven off the systems they care about integrating with. And that new, tight, piece of software ends up being much more fit for purpose with full control of new features given to company deploying it. While this was always the case (buy vs build), AI changes the CapEx/OpEX for the build case.

>>d_watt+o2
Pretty much. My employer was looking to cut costs and they were spending ~500k a year on a product that does little more than map entra roles/groups to datasets and integrated with a federated query engine through a plugin. Took a couple days to build a replacement. The product had only a few features we needed.

>>gritsp+He
As niche SaaS provider, I'm trying to avoid succumbing to the same fate. The product I built carefully for years would now be within the reach of a senior dev with a couple focused weeks -- if they knew all the requirements. To avoid being overtaken, I'm working to increase my customer's requirements -- getting them hooked on new reports and features I never had time to build before LLMs could do it for me. This makes it less likely for a competitor to be able to afford to quickly replace me.

At the same time, I have no idea what the cost of LLMs usage will be in the future. So I'm working to ensure the architecture stays clean and maintainable for humans in case this kind of tooling becomes untenable.