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SITUATION EXPLAINED: Coatue just published a chart showing how fast AI agents are replacing internal white-collar functions at OpenAI itself. • Chart tracks what percent of OpenAI's internal output tokens are generated by Codex vs ChatGPT • Engineering crossed 50% Codex usage at end of 2025, now basically 100%... show more

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18,841 views • 1 month ago •via X (Twitter)

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a16z

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As the Musk-vs-Altman fight is becoming pay-per-view TV, xAI is rebuilding, Anthropic is surging, and the economics of intelligence are starting to break old models, all while OpenAI sits on a $852B war chest. -- xAI admits it “was not built right the first time,” and Colossus 2 is now reportedly training 7 models at once. -- Software engineering roles are up 67,000, while roughly 80,000 layoffs hit other white-collar functions in Q1. -- Anthropic is making the biggest product pivot in AI from systems that answer questions to managed agents that actually do the work. - South Korea is mandating 40% solar rooftops, while the DOE is backing $800M in microreactors.

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45,277 views • 1 month ago

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The Transcript

186,329 views • 3 months ago

The same signals that marked the bottom are now showing up as signs of a local top "A lot of the signals we saw that were very key signs of a bottom are now showing up as key signs of a local top. I do not think this move is over, I'm just getting excited to hopefully buy some dips as price drops here. Bitcoin has a tendency not to give you the dips you want, but that's what I'm looking for" "Those signals are overbought RSI now. We had almost historically high RSI on the daily chart. We're seeing bearish divergence with overbought RSI on lower timeframes, meaning strength abating even as price continued up" "And we're at key levels of resistance. The 50 moving average on the weekly chart, we basically went right up to it and got rejected. That's the kind of area you'd be looking for price to retrace below, to gather up strength to go back above"

The same signals that marked the bottom are now showing up as signs of a local top "A lot of the signals we saw that were very key signs of a bottom are now showing up as key signs of a local top. I do not think this move is over, I'm just getting excited to hopefully buy some dips as price drops here. Bitcoin has a tendency not to give you the dips you want, but that's what I'm looking for" "Those signals are overbought RSI now. We had almost historically high RSI on the daily chart. We're seeing bearish divergence with overbought RSI on lower timeframes, meaning strength abating even as price continued up" "And we're at key levels of resistance. The 50 moving average on the weekly chart, we basically went right up to it and got rejected. That's the kind of area you'd be looking for price to retrace below, to gather up strength to go back above"

The Wolf Of All Streets

34,989 views • 9 days ago

Lead of Claude Code: "100% of our code at Anthropic is now shipped by Claude. Over 30% of it is written using /loops. Loop engineering is the future of coding. Loops are as big a shift as move from source coding to agents" in a 1-hour podcast, a Claude Code lead engineer shares the internal setup for building the #1 coding tool. It’s the most useful breakdown of how the Anthropic team uses Claude internally that I’ve seen on YouTube. Worth more than a $500 vibe-coding course.

Lead of Claude Code: "100% of our code at Anthropic is now shipped by Claude. Over 30% of it is written using /loops. Loop engineering is the future of coding. Loops are as big a shift as move from source coding to agents" in a 1-hour podcast, a Claude Code lead engineer shares the internal setup for building the #1 coding tool. It’s the most useful breakdown of how the Anthropic team uses Claude internally that I’ve seen on YouTube. Worth more than a $500 vibe-coding course.

Codez

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Happy to properly launch Anna, the proactive AI agent for parents! Uncovering a bit of the technology behind the scenes! Building Anna is where I learned: 💾 Memory as plain text sucks. You need structured memory. Like a full-blown PostgreSQL DB that stores your tasks and calendar in a structured manner. Most harnesses are good at coding-related stuff. Let it do the query. Don't let it vibe-search the memory. Let it vibe your SQL query 💭 Dreaming is a useful concept for enhancing memory to feed the LLM context. But DO NOT vibe your dream. Asking your agent to "hey, just dream and keep the relevant memory around" is a recipe for deleting a bunch of important information and keeping trash around. Your dream needs to have some Taxonomy (or better, Ontology). What information is important? For who? With what object? What can they do? And again, these are impossible to describe and act well without a proper schema 🔄 Loop Engineering is important for smoothing out rough edges in the system we build. But even expensive loop engineering with a state-of-the-art model can't out-engineer bad system design. The highest leverage an AI Engineer can do is actually building the right system design, and having an eye on both product delight and engineering scalability There are several more insights that I plan to cover in a dedicated video about Agentic AI Engineering. But it's actually a huge relief that the future of software engineering... is still software engineering

Happy to properly launch Anna, the proactive AI agent for parents! Uncovering a bit of the technology behind the scenes! Building Anna is where I learned: 💾 Memory as plain text sucks. You need structured memory. Like a full-blown PostgreSQL DB that stores your tasks and calendar in a structured manner. Most harnesses are good at coding-related stuff. Let it do the query. Don't let it vibe-search the memory. Let it vibe your SQL query 💭 Dreaming is a useful concept for enhancing memory to feed the LLM context. But DO NOT vibe your dream. Asking your agent to "hey, just dream and keep the relevant memory around" is a recipe for deleting a bunch of important information and keeping trash around. Your dream needs to have some Taxonomy (or better, Ontology). What information is important? For who? With what object? What can they do? And again, these are impossible to describe and act well without a proper schema 🔄 Loop Engineering is important for smoothing out rough edges in the system we build. But even expensive loop engineering with a state-of-the-art model can't out-engineer bad system design. The highest leverage an AI Engineer can do is actually building the right system design, and having an eye on both product delight and engineering scalability There are several more insights that I plan to cover in a dedicated video about Agentic AI Engineering. But it's actually a huge relief that the future of software engineering... is still software engineering

Gogo | Dota for Toxicity

30,766 views • 2 months ago

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"I ran a startup for 5 years. I thought we moved fast. Then I joined OpenAI—and realized I had no idea what fast actually meant." Head of Product for OpenAI's Codex Alexander Embiricos and I chat about: 🔸 How OpenAI built the Sora Android app (which hit #1) in just 18 days by using Codex 🔸 Codex growing 20x since August 🔸 How they're thinking about the dev bottleneck shifting from coding to reviewing AI-generated work 🔸 Why coding will be a core competency for every AI agent—because writing code is how agents use computers best 🔸 Why the bottleneck to AGI-level productivity isn’t model capability—it’s human typing speed 🔸 The vision of Codex, and how they plan to win Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 Fin — The #1 AI agent for customer service: 🏆 Atlassian Jira Product Discovery — Confidence to build the right thing:

Lenny Rachitsky

334,564 views • 8 months ago

Tonight I sat back from my screen and couldn't speak. Computational engineering logic fused with modern CAD, and what came out wasn't just a design. It was a glimpse. Every generation thinks it's living at the hinge of history. Ours actually is! The machines we imagined in dark theaters as kids are being born right now, not in billion-dollar labs, but at desks like mine. And you can start building them.

Tonight I sat back from my screen and couldn't speak. Computational engineering logic fused with modern CAD, and what came out wasn't just a design. It was a glimpse. Every generation thinks it's living at the hinge of history. Ours actually is! The machines we imagined in dark theaters as kids are being born right now, not in billion-dollar labs, but at desks like mine. And you can start building them.

Dylan Small

48,200 views • 1 month ago

White-collar jobs are getting hit harder by AI than blue-collar ones right now. Tasks like writing, data analysis, coding, research, and admin work are exactly what current AI excels at, unlike physical manufacturing work. One analysis puts white-collar automation potential at around 30%, compared to under 1% for blue-collar roles. Goldman Sachs says AI could automate 25% of all US work hours, with 300 million jobs globally exposed, though actual displacement over 10 years might be more like 6-7% of workers. What’s actually happening: AI was linked to about 4.5% of job losses in 2025, and in April 2026 it accounted for 26% of layoffs in one report. Entry-level white-collar roles, junior analysts, coders, paralegals, customer service, are seeing the biggest squeeze, with hiring slowing sharply in AI-exposed fields. BCG projects 50-55% of US jobs will change significantly in the next couple years, but only 10-15% get fully eliminated in the next five. A lot of roles are getting augmented, people using AI tools to work faster rather than being replaced outright. It’s still early, so the big wave of losses some CEOs are warning about hasn’t fully materialized yet. The real story so far is fewer new office jobs being created, especially at the entry level.

White-collar jobs are getting hit harder by AI than blue-collar ones right now. Tasks like writing, data analysis, coding, research, and admin work are exactly what current AI excels at, unlike physical manufacturing work. One analysis puts white-collar automation potential at around 30%, compared to under 1% for blue-collar roles. Goldman Sachs says AI could automate 25% of all US work hours, with 300 million jobs globally exposed, though actual displacement over 10 years might be more like 6-7% of workers. What’s actually happening: AI was linked to about 4.5% of job losses in 2025, and in April 2026 it accounted for 26% of layoffs in one report. Entry-level white-collar roles, junior analysts, coders, paralegals, customer service, are seeing the biggest squeeze, with hiring slowing sharply in AI-exposed fields. BCG projects 50-55% of US jobs will change significantly in the next couple years, but only 10-15% get fully eliminated in the next five. A lot of roles are getting augmented, people using AI tools to work faster rather than being replaced outright. It’s still early, so the big wave of losses some CEOs are warning about hasn’t fully materialized yet. The real story so far is fewer new office jobs being created, especially at the entry level.

Patricia 🇺🇸

74,890 views • 3 months ago

Three months ago, Codex was trash for knowledge work. Now it's my daily driver. I use it for writing, recruiting, deep engineering work, and everything in between. It even keeps me at inbox 0. I chatted with Every 🧱's head of growth Austin Austin Tedesco on Every 🧱's AI & I about what changed, and why he now spends 80% of his working time in the Codex desktop app too. We get into: - How Codex went from making Austin feel like an idiot to being the place he goes to get stuff done, including complex tasks like writing go-to-market plans using existing material from Slack, Notion, and meeting transcripts. - Why the Codex’s desktop app, which is faster and more reliable than Claude Desktop/Cowork, is the real differentiator. - How I source candidates with Codex by having it identify career arcs, not keywords—my go-to move is identifying organizations likely to teach the skills Every needs for a role, and then find candidates from that pool who have since gone on to work in AI. This is a must-watch for anyone who's wondering whether it’s finally time to give Codex a try. Watch below! Timestamps How Codex went from a tool for senior engineers to a daily driver for knowledge work: 00:00:57 How Claude Code proved that a great coding agent works for any knowledge work: 00:02:42 Austin's switch to Codex: 00:07:24 How Austin set up Codex with folders, keys, and reviewer agents: 00:13:48 Using Codex to brainstorm automations across Gmail, Slack, and Notion: 00:18:24 How Austin manages the human review step when Codex is drafting communications: 00:22:42 Using Codex to build specialized agents inspired by product executive Claire Vo: 00:28:54 Synthesizing meeting transcripts and Slack threads into a go-to-market plan: 00:31:09 Building a live KPI tracker in Notion that agents can read: 00:40:15 Using Codex for recruiting: 00:44:54

Three months ago, Codex was trash for knowledge work. Now it's my daily driver. I use it for writing, recruiting, deep engineering work, and everything in between. It even keeps me at inbox 0. I chatted with Every 🧱's head of growth Austin Austin Tedesco on Every 🧱's AI & I about what changed, and why he now spends 80% of his working time in the Codex desktop app too. We get into: - How Codex went from making Austin feel like an idiot to being the place he goes to get stuff done, including complex tasks like writing go-to-market plans using existing material from Slack, Notion, and meeting transcripts. - Why the Codex’s desktop app, which is faster and more reliable than Claude Desktop/Cowork, is the real differentiator. - How I source candidates with Codex by having it identify career arcs, not keywords—my go-to move is identifying organizations likely to teach the skills Every needs for a role, and then find candidates from that pool who have since gone on to work in AI. This is a must-watch for anyone who's wondering whether it’s finally time to give Codex a try. Watch below! Timestamps How Codex went from a tool for senior engineers to a daily driver for knowledge work: 00:00:57 How Claude Code proved that a great coding agent works for any knowledge work: 00:02:42 Austin's switch to Codex: 00:07:24 How Austin set up Codex with folders, keys, and reviewer agents: 00:13:48 Using Codex to brainstorm automations across Gmail, Slack, and Notion: 00:18:24 How Austin manages the human review step when Codex is drafting communications: 00:22:42 Using Codex to build specialized agents inspired by product executive Claire Vo: 00:28:54 Synthesizing meeting transcripts and Slack threads into a go-to-market plan: 00:31:09 Building a live KPI tracker in Notion that agents can read: 00:40:15 Using Codex for recruiting: 00:44:54

Dan Shipper 📧

55,561 views • 4 months ago