Mainframe migration quote fell from $200 million to $20 million, Mollick recounts

Ethan Mollick said a company he spoke to went from a $200 million data migration plan for its old mainframe data to a $20 million plan a few months ago, and its CTO is thinking they may do it for $2 million in a couple of months (14:20). The company is not named.

(14:18) Like I just spoke to a company, they went from a $200 million data migration plan for their old, you know, old mainframe data to they renegotiate to a $20 million plan just a few months ago, and the CTO is thinking like, well, maybe I can do this for $2 million in a couple months.

Mollick said the change "involves literally pulling apart teams and thinking about what do we want to do and resetting projects from the beginning" (14:35).

(14:35) but it involves literally pulling apart teams and thinking about what do we want to do and resetting projects from the beginning.

Mollick described pulling apart teams as: "pull out your best senior engineer, put them with subject matter expert and a marketing and strategy person, and then have them do like impossible projects" (14:10). The auto-captions render this sentence with minor errors.

(14:10) called disbursement, you pull out your best senior engineer, put them with subject matter expert and a marketing and strategy person, and then have them do like impossible projects.

Mollick said other organisations he sees are "trying to think about how do we take teams of highly talented people" and "give them really hard challenges to do, high token budgets, and seeing how far they get" (15:05).

(15:05) Um but other organizations I see are trying to think about how do we take teams of highly talented people, these get people already getting returns from AI, put them together, give them really hard challenges to do, high token budgets, and seeing how far they get.

Mollick said "you don't get organizational productivity gains instantaneously from individual productivity gains" (02:11), in reply to Kantrowitz citing a Dallas Fed study that high-AI-exposure jobs show less productivity growth than during COVID (01:20).

(02:11) you don't get organizational productivity gains instantaneously from individual productivity gains. Kantrowitz at 01:20-02:04: this is from the Dallas Fed ... high AI exposure jobs are seeing more productivity gains than the low AI exposure jobs, but they're actually seeing less productivity gains than they saw during COVID

Mollick said "token maxing was big this spring, and then it was like, 'Oh, no, everyone's spending too much on tokens'" (07:53).

(07:53) Like, token maxing was big this spring, and then it was like, "Oh, no, everyone's spending too much on tokens."

Mollick said "people are worried about token costs, but that's often a management problem" (09:35).

(09:35) And I think people are worried about token costs, but that's often a management problem.

Replying to Kantrowitz's remark about using AI to solve a millennium problem in maths, Mollick said there was a coordination layer: "There was what, 10,000 agents passing 8 million messages back and forth, right?" (39:59). He gave the figures as a recollection, and no primary for the result has been found.

(39:56) Well, and the millennium problem adds a different piece. There's a coordination layer between There was There was what, 10,000 agents passing 8 million messages back and forth, right?

The video was published on the Big Technology Podcast YouTube channel on 25 October 2026, titled "This AI Expert Argues We're Underestimating AI's Impact — With Ethan Mollick"; Alex Kantrowitz is the interviewer.

Watch page metadata: publishDate 2026-10-25T16:00:10-07:00; title: This AI Expert Argues We're Underestimating AI's Impact - With Ethan Mollick

Ethan Mollick is Associate Professor of Management at the Wharton School of the University of Pennsylvania and Co-Director of Generative AI Labs at Wharton.