How Agentic AI Really Gets Priced and What Founders Get Wrong
カートのアイテムが多すぎます
カートに追加できませんでした。
ウィッシュリストに追加できませんでした。
ほしい物リストの削除に失敗しました。
ポッドキャストのフォローに失敗しました
ポッドキャストのフォロー解除に失敗しました
How Agentic AI Really Gets Priced and What Founders Get Wrong
-
ナレーター：
-
著者：
概要
Agentic AI is everywhere right now, but most teams are still struggling to price it, deploy it, and make it work in production.
In this Fireside Chat, Ravi Belani sits down with Vibhor Rastogi, who leads AI investing at Citi Ventures, to unpack what’s actually happening inside enterprise AI adoption and what founders often get wrong.
They dive into why pricing agentic software is still an unsolved problem, what’s genuinely overhyped in the “agent” wave, and how startups can build defensible AI businesses in a world of reskinned workflows and runaway costs.
Topics covered include:
Why seat-based, consumption-based, and outcome-based pricing all break in different ways
How CIOs think about predictability, guardrails, and runaway AI costs
What investors really want to see from agentic AI startups
Why many “agents” today are just rebranded APIs and workflows
The importance of memory, learning, and real autonomy in agentic systems
How founders can solve the data cold-start problem through design partnerships
Why an “Agentic Ops” layer may be the missing piece for enterprise adoption
If you’re building, investing in, or deploying agentic AI, this episode will help you separate signal from noise.