Liftoff with Keith Newman: Episode Summary
Title: Why AI is Broken: RK Anand Exposes the Hidden Costs and Challenges
Release Date: July 2, 2025
Guest: RK Anand, Co-Founder and Chief Product Officer of Recogni
Introduction
In this enlightening episode of Liftoff with Keith Newman, former journalist and Silicon Valley dealmaker Keith Newman engages in a deep conversation with RK Anand, the co-founder and Chief Product Officer of Recogni. The discussion delves into the intricate economics of AI, particularly focusing on the challenges and hidden costs that accompany the evolving landscape of artificial intelligence.
Tokenized AI Economics and Variable Compute
The conversation kicks off with an exploration of the traditional token-based economy in AI consumption. RK Anand explains how tokens have served as a currency equivalent, simplifying the billing process based on input and output tokens. However, with the advent of more complex AI models, this system faces significant challenges.
RK Anand [00:33]: “...there has to be a reassessment in the industry on how tokens are a currency and maybe some change in how you think about how you can use AI, how you can charge for it, and how you can be profitable with AI.”
As AI models become more sophisticated, particularly with reasoning capabilities, the fixed compute and energy consumption per token become variable, complicating the existing economic framework.
Impact of Chain of Thought and Agentic Models on Compute Costs
RK delves deeper into the technical advancements of AI, highlighting the transition from simple language models to chain of thought and agentic models. These advanced models significantly increase compute consumption, leading to exponential cost escalations.
RK Anand [02:03]: “...when you went from a language model to a chain of thought model, costs could go up by anywhere from 10x to 100x.”
The introduction of agentic models, which involve multiple interacting models to perform tasks like customer service or travel planning, further amplifies compute usage. This multiplicative effect poses a substantial economic burden on AI service providers.
Challenges in AI Infrastructure and Profitability
The rising costs associated with advanced AI models bring forth questions about the sustainability and profitability of AI-driven businesses. RK Anand emphasizes the industry's struggle to monetize inference effectively without compromising profitability.
RK Anand [03:05]: “...nobody's yet figured out how to people are making revenue in inference, but are they making profits? Is the question.”
The variability in compute consumption necessitates a reevaluation of pricing strategies and business models to ensure that AI applications remain economically viable.
Recogni's Role in AI Infrastructure
Addressing these challenges, RK introduces Recogni's mission to revolutionize AI infrastructure. As an AI infrastructure and inference company, Recogni focuses on developing highly efficient chips and systems to lower the cost of AI computation.
RK Anand [05:05]: “...we are trying to change the economics of inference by building technology that has far much more higher efficiency in terms of compute costs and compute power consumption.”
By enhancing compute efficiency, Recogni aims to make AI services more affordable and sustainable, benefiting both model providers and application developers.
Energy Constraints and Infrastructure Solutions
A significant portion of the discussion revolves around the energy constraints facing AI infrastructure, particularly in the US and EU. RK Anand outlines the challenges of energy acquisition and the limitations of existing power transmission infrastructure.
RK Anand [06:30]: “...we are constrained in energy power. And that is a major, major issue in the us, in EU and in many, many countries.”
Recogni proposes building data centers with captive power sources, such as gas turbines, to ensure a stable and efficient power supply. This strategy aims to support the growing demand for AI compute without overburdening existing energy infrastructure.
RK Anand [07:30]: “...you actually have to have AI hardware and infrastructure systems that deliver more tokens per rack, consume much lower power per rack, and do it much less expensively.”
By addressing both compute efficiency and energy consumption, Recogni seeks to sustain the momentum of AI development and its broader application across various industries.
Conclusion
The episode wraps up with RK Anand emphasizing Recogni's commitment to building technology that supports the expansive and sustainable growth of AI. By tackling the economic and infrastructural challenges, Recogni aims to ensure that AI remains a driving force for productivity and innovation.
RK Anand [08:19]: “...the use of AI and its broader ubiquitous use the virtuous cycle, the flywheel will get slowed down and that's not what we want.”
Keith Newman and RK Anand part ways with a shared vision of advancing AI infrastructure to foster continued technological progress and societal benefits.
This episode offers a comprehensive look into the economic and infrastructural hurdles facing the AI industry today. RK Anand's insights shed light on the necessary innovations required to sustain AI's growth and its integration into everyday life.