AI Output Is Becoming Cheap. Operational Value Isn’t.

[Georgii Grigoriants]

Over the last few weeks, I’ve noticed more and more investors talking about AI agents, automated underwriting tools, AI workflows, and “fully automated” deal analysis.

Some of it is genuinely useful.

But real estate may be entering the same phase the software world is starting to encounter now:

AI systems that generate enormous amounts of activity without creating proportional operational value.

An AI tool that reduces underwriting time from 8 hours to 2 hours is valuable.

An AI tool that generates beautiful reports, endless summaries, and impressive-looking analysis — while the investor still manually verifies everything afterward — may simply become expensive operational noise.

The real question is no longer:
“Can AI generate output?”

The real question is:
“What measurable outcome actually improved?”

Did the system:

• reduce underwriting time?
• identify risks earlier?
• improve screening quality?
• accelerate decision-making?
• reduce expensive mistakes?

Or did it simply produce more analysis?

As AI becomes increasingly embedded into real estate workflows, the most valuable systems probably won’t be the ones generating the most output.

They’ll be the ones most tightly connected to real operational outcomes.

[Bill B.]

I’d like to go more than a week without seeing an example of obviously incorrect results being produced before I trust it with a purchase costing more than $1,000. 

Maybe once the language models are “trained” they can tell them to ignore 90-95% of the internet and only take results from “trusted” sources. 

You’re going to either make employees totally “held harmless” for bad ai results that cost the company millions, or you’re going to make them privately verify all the information. When your company makes a catastrophic mistake can the employee just say the info came straight from AI and it’s not their fault? How many times can they do that?