The cost of building and running AI, and how money moves through the market.
Trend this week
About the same5%of conversation
11 of 11 published weeks met the cross-podcast threshold.
5quoted moments3episodes3podcasts discussing this topic
A closer read
What changed
Its share rose from 4.9% to 5%, with evidence across 7 podcasts.
Where it showed up
It appeared in episodes including “How AI Changed This Summer” and “#236: AI Answers - No Time for AI, AI Budgets, Vendor Terms & Data Risk, AI Disclosure & Vanishing Entry Level Roles”.
How to read it
3 cited episodes from 3 podcasts are shown below. The topic appeared across 7 podcasts in the full analysis.
“the market started to understand that the total addressable market was not, in fact, number of knowledge workers times $20 per seat per month, but could be hundreds or even thousands of dollars for those same knowledge workers each month.”
“I can imagine that's going to be difficult. Um, it says vendors like Salesforce now tie their prices directly to the revenue gains or cost cuts customers see. So it's got to be about value, not just about buy more tokens, token max, and so on.”
“At the higher level, you understand that deeper work, let's say, costs more money, but how that translates into tokens and token usage when you hear that term flown around— but um there's no easy way to monitor that, or at least if it exists, most people don't know that it exi...”
Monday's first guest was Zak Bratton-Glennon, a general partner at Gradient, the AI seed fund that launched inside Google in 2017 and spun out of Alphabet last October. His written thesis: open models have closed most of the gap on coding, and what remains is domain judgement ...
pair them up with AI agents, which can really read the code, understand the code, and some expertise of other more senior members of the team, and they can do a lot more than what someone could have done maybe 12 or 18 months ago.
#235: OpenAI-Hugging Face Hack Involved 100s of Agents, Bill Gates Now Pessimistic on Jobs, Nvidia Doubles Revenue & Anthropic Targets “$30 Trillion” TAM
All right, to wrap up here, we've got some AI product and funding updates I'll go through real quick as we close out the episode. So first up, OpenAI has talked through some of the first results with Jalapeño, its first custom inference chip. It says this system developed with...
Its share grew from 10.7% last week to 13.7% this week.
Share of this week’s podcast conversation
Recent weeks10.8%→This week13.7%
Came up more often than in recent weeks.
Podcast moments5 cited momentsViewClose
The Cognitive Revolution
AI:AM Highlights: Welcome to the AGI Era
Wednesday's other fight. Dean Ball, who now leads a strategy team at OpenAI, had published an essay apologising for years of understating AI risk in public. I and many of my colleagues largely failed to talk about this issue with the seriousness and urgency it required. David ...
how do you ensure that it behaves within guardrails, right? It behaves in a way that is um suitable for your enterprise, right? So, the problem of governance and assurance becomes very important.
