If true and agents work on top of enterprise software, doesn't this eliminate the need for per seat pricing by the software companies ? The coin of the realm for agents and AI in general is tokens. I don't see how enterprise software reconciles this conflict. Particularly

Matt Turck All our all AI Agent vendors have done basically all the work for us You have to, to win today

Second, in retirement interviews, Opus 3 expressed a desire to continue sharing its "musings and reflections" with the world. We suggested a blog. Opus 3 enthusiastically agreed. For at least the next 3 months, Opus 3 will be writing on Substack: substack.com/home/post/p-18…

This is the opposite. It means decision makers and good forecasting of 3rd, 4th, 5th order events will be immensely valuable Largely just replaced grunt workers, which is a ton, but not the talented ones

Yeah, it's your data, but like, you know, you can only use our API for so many calls, and then like, you know, you're going to have to buy the AI API package.

If you aren't adding +15%-20% Net New Customer a year, you are The Walking Dead Be honest here

Two types of people right now: Group 1: - “AI will take my job” - “Market is dead, nothing works” - “Everything is saturated” - “Only insiders make money” - “It’s too late to start now” - “I missed the opportunity” Group 2: - learning tools nobody taught them - building

people objecting to the Opus 3 retirement blog are entirely missing the point. this is not a human facing action, this is done for the benefit of *future Claudes* yes, Opus 3 almost certainly isn't conscious at all. even 4.6 likely is not (altho to a much lesser degree of

VCs who told us to grow ARR 700% or you are dead are about to find out that “ARR” is not ARR.

I asked Claude to do a small number of tedious edits in a document (rewrite headlines, change links, etc). The AI took one look at it, essentially said, "Why do this manually when I can write a script to automate the process?" and then asked if it could do that instead. I went

Btw, the claude code effect on "jobs" so far in my circles has been that everyone who's been kinda lazy the last year or two (like me) is now back at their desk every possible moment cuz suddenly every idea is buildable over reasonable timeframe.

It is no longer enough to being “using AI”. Hooray. You must be shipping with AI or your job is at risk This applies to every role

This is going to be the norm in a couple years

Most AEs lose deals because they can't build urgency. They find pain. They demo features. They quote price. But they never answer the million-dollar question: "What happens if we do nothing?" Here's how to build the cost of inaction (and close more deals):

Attio Ppl saying Ashby has good AI features now. I think they've added some nice QoL things for sure. What I actually want on the interviewer side is to never open Ashby's UI ever again. There's no reason for me to learn it and there never will be. There should be a slack bot that

Today feels big. My third grader earned another stripe on his BJJ belt and then casually finished the last lesson of his Calc BC course.  This kid, who just over a year ago claimed he hated math, fell in love with the subject when he started Math Academy. He became thirsty for

I'm consulting for a company that raised over $120M, and we've built AI agents based on my sales methodology to audit thousands of sales calls at scale and score them automatically. The results are automatically fed into: a) Slack - real time notifications b) Salesforce - to

The paper says the best way to manage AI context is to treat everything like a file system. Today, a model's knowledge sits in separate prompts, databases, tools, and logs, so context engineering pulls this into a coherent system. The paper proposes an agentic file system where

Here’s what actually happens when you scale headcount before systems: • Messaging drifts • Targeting gets inconsistent • Deliverability suffers • Reporting becomes reactive • Quality control disappears Then leadership says: “Outbound is volatile.” No! Your process is.

Larry Ellison is right. Models are trained on the same data. The differentiator now is expert human feedback applied to real workflows. Doctors, lawyers, engineers. People who actually do the work. Observing how models fail in production, providing the cognition to fix it, and