alt Hacker NewsI 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.
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And in many cases, it's 12 features, with 2 of the features not even existing in the big SaaS.
I'm pretty sure every developer who has dealt with janky workflows in products like Jira has planned out their own version that fits like a glove, "if only I had more time".
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.
there's no shortage of software engineers, if it was so easy for an organization to replace a saas with something built in-house they'd be doing it all the time. In my experience in enterprise consulting implementing a well defined requirement is the easiest part. Getting everyone to agree on the requirement, getting it defined, and stopping it from changing after every demo is the hard part.
Exactly, a lot more focus -- and most importantly specific domain knowledge -- allows the end-user to build exactly what they need, fast.
Until a given company decides they need access control for their contractors that's different from their employees, etc. etc. etc. - seen it all before with internal often data scientist written applications that they then try to scale out and run into the security nightmare and lack of support internally for developing and taking forward. Usually these things fizzle out when someone leaves and it stops working.