AI Rising Podcast

There are genuine practitioner insights buried in this episode - enterprise POC-to-production failure modes, agent ops/finops parallels to cloud economics, and the governance gap blocking agentic deployments - but they are severely diluted by a multi-minute AI Appreciation Day rant, a rambling introduction, and repetitive back-and-forth that a prepared reader could skip entirely.

“2024 till December 2024, I can say that most of our customers were trying to build that context that, uh, how can I use this within my organization to able to solve or automate say one business process? And they were able to do that in POC stages in some form or the other. But there were various questions around security and data privacy and all that came through which made the bridge too far where they could not move from staging environment to production.”

“the cost to achieve is going higher and the returns, which has been promising is something that, you know, it's got a curve, right?”

The bill-shock analogy mapping cloud sprawl to AI token costs is the sharpest original moment, and the framing of agents-as-SaaS replacing consulting headcount is interesting but underexplored; the rest is a rotation of standard AI talking points - human in the loop, find the right use case, LLMs have a training cutoff, India has talent - that circulate widely.

“you remember Leslie, when the cloud came in and everyone was given a cloud, uh, playing area and then they just said that we'll just put the credit card and suddenly got a $5,000 bill shocks.”

“you'll not lose your job to AI. You will maybe lose your job to someone who knows AI.”

Praveen Oja is a genuine enterprise practitioner - EPAM India's chief technologist dealing with real client deployments daily - which gives the conversation grounded credibility, but he is not a hyper-senior operator who has built or scaled something singular; his insights reflect broad advisory exposure rather than deep proprietary experience at one major deployment.

“compliance is one of the areas which uh, is very strongly, uh, you know, uh, we are pursuing this opportunity because imagine the amount of surveillance, uh, uh, infrastructure that you had to earlier build, you know, to track that somebody is giving off insider trading information or not.”

“today I have an uh, LLM deployed on my MacBook and I do my own MCP research and A2 agents and tool creations pretty much locally.”

The episode includes a handful of real data points - Statista's $351B search ad figure, Gartner's 40% project failure prediction, NASSCOM's 16,000 GCC projection, $10M+ OpenAI consulting deal sizes - but guest commentary on actual client deployments is consistently vague, with no named enterprises, no outcome metrics, and no concrete timelines attached to claimed results.

“search advertising is almost like 351 uh billion in 2025 as per Statista. Uh, uh I think 75% of Google chromes uh, uh ad stuff comes you know uh, ad revenue comes from search.”

“get to 16,000 by 2030 or something by NASCOM Projection”

The hosts occasionally land a sharp, timely question - particularly on OpenAI's consulting pivot and the Palantir parallel - but the overall dynamic is loose and self-indulgent: hosts frequently answer their own questions, interrupt substantive answers for tangents, and rarely push back on vague claims or ask for harder evidence.

“what do you make of this news that came out this week that uh, OpenAI is getting into the consulting business for $10 million plus accounts and they are using Palantir's uh, playbook of actually forward deploying the engineers.”

“But, uh, Praveen, what are your clients asking you? Because you're dealing with, you know, digital transformation in the engineering space and across manufacturing industries”