In sum, if one builds solid paddocks, and operates from an “it is just another tool” in the system architecture point-of-view… I think it can be helpful.

But one must likely invest billions, first (as Merck has) — to build both the plumbing, and the guard-rails — to prevent it from “wilding / slopping / hallucinating” in any final product or document. And human beings — with deep experience and critical eyes — must closely review all its output. That’s a given.

Specifically, note that Merck is seeing it as being most-effective, in writing FDA compliant marketing materials, with a 99% accuracy experience. [It seems far less useful, in discovering and designing actual chemical entities — i.e., drugs.] Why? Because that process relies on… the creativity of a very very experienced mind, or set of minds — after exploring and abandoning perhaps thousands of blind alleys.

We shall see. In any event, here is the slightly-breathless piece, in a tech mag booster report:

…Merck’s plumbing-first strategy comes from lessons learned during the early days of cloud in the 2010s “when nobody knew what the heck was going on,” Finnerty said.

Getting the cloud right meant building from the ground up; at Merck, that infrastructure now supports 2,500 AWS accounts, numerous Microsoft Azure subscriptions, and new Google Cloud Platform (GCP) integrations.

“AI is gonna be the same exact thing,” Finnerty said. “We’re going to have thousands and thousands of agents.” The questions then pile up: How do you register them? How do you secure them? How do you ensure they’re connected to the right tools, and have access to the right data and the right context?

Context delivery is also critical; Merck works with three hyperscalers and has forty-seven edge locations and hundreds of databases. “Many, many petabytes” of structured and unstructured data are stored in Oracle databases, SQL databases, Excel spreadsheets, phone transcripts, and other repositories, Finnerty said….

His team is building scaffolding to deliver meaningful context in various situations, he explained. Data must be organized and ingested into various platforms, because “there’s no one solution to solve every single problem.” Sometimes it’s Databricks, other times it’s Amazon Redshift, “plus four other things….”

It is cool that both Amazon and Oracle win shout-outs, in the VentureBeat piece. Heh. Onward, smiling — just the same.

नमस्ते