AI News & Strategy Daily with Nate B. Jones
Daily AI strategy and news for the AI curious, builders, and executives. Host Nate B. Jones, a 20-year product leader and AI strategist, cuts through hype and generic advice with practical frameworks and workflows. The podcast offers guidance tested in real organizations, with new videos every day on YouTube and deeper analysis available via a newsletter.
Episodes
Narrow Superintelligence: Why AI Strengths May Not Transfer
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What does superhuman performance in mathematics tell us about the future of work? Nate examines OpenAI's mathematical research release and why strong performance in one domain does not automatically transfer to software engineering or business judgment.Why checkable mathematical results differ from maintainable software.Ho
5 Human Skills AI Makes More Valuable and How to Build Them
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What can you take on now that AI can help with more of the work? Nate revisits five human qualities that become more useful as AI grows more capable: taste, agency, focus, resourcefulness, and follow-through.How taste turns care about a problem into something you can build and test.Why agency means making an attempt and learni
Google's Spirit Airlines Bid: Work Data for AI Training
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What is your work actually worth—and who gets paid when its records are sold? Nate examines the proposed sale of Spirit Airlines data and the broader market for company records used to train AI agents.He distinguishes the work that creates value from the messages, documents, and presentations left behind. Through an invoice ex
Gemini 4 Argon and the Benchmark That Matters: Customers
For deeper playbooks and analysis: https://natesnewsletter.substack.com/Can Google and Meta actually catch up in AI? Nate argues that customer obsession is becoming the new benchmark. He looks at who the major labs serve, how dots and Muse complement each other, and why useful product experiences matter more than leaderboard positions.From sorting a personal library to helping people manage everyd
How AI agents are changing the way you buy software
For deeper playbooks and analysis: https://natesnewsletter.substack.com/Better agents are changing what software is worth. Nate examines how agents change the value of data, applications, and the workflows people build around them, from DoorDash to Microsoft, Salesforce, and recruiting.He explains why a tool can become easier to leave while a trained agent becomes harder to replace, and what softw
OpenAI DevDay 2026: Dots, ChatGPT Space, and GPT-6.1 Sol
For deeper playbooks and analysis: https://natesnewsletter.substack.com/The agent wars are here. Nate examines OpenAI DevDay through the jobs agents actually get right: ongoing responsibilities with Dots, shared work in Space, efficient reasoning with Sol, and new opportunities for builders.He compares agent form factors with practical utility, explains why context matters, and weighs faster model
Microsoft's Autopilot Agent: 5 AI Habits to Build at Work
For deeper playbooks and analysis: https://natesnewsletter.substack.com/Microsoft is bringing agent-style work into the tools people already use. Nate examines what Autopilot changes and five practical skills for getting better results from AI: defining the assignment, supplying the right context, making useful work repeat, matching reasoning to difficulty, and improving the next run.He explores w
Claude Opus 5.5 Review: Easier to Steer, Fewer Tokens
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What does an AI task actually cost when you count the whole job? Nate examines his Opus 5.5 LEGO build, the difference between API pricing and a subscription allowance, and why fewer retries can matter as much as the price per token.He also explores writing that preserves your intent, clearer stopping conditions for overnight
An AI assistant added up my subscriptions: $5,350 a year. The prompt guide to get your own list in about 20 minutes.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What makes a consumer AI assistant useful enough for people to return to it? Nate examines Meta’s Muse, the everyday work it can take off people’s plates, and what its conflict with Amazon reveals about attention and commerce.Finding recurring costs and acting on subscriptions.Making complex AI work feel simple.Why the shoppin
How to Scale AI Developer Productivity Across a Team
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What helps a team turn faster AI coding into useful work that actually ships? Nate examines the setup behind Lauren Tan’s self-reported pull-request volume and six principles teams can apply to their own agents.Share useful agent work across the team.Preserve history outside a temporary chat.Keep humans accountable and give ag
NVIDIA World Models Explained: What Developers Can Build
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What does a world model actually do—and why might a robot learn to fold your laundry before it can make perfect scrambled eggs?Nate sits down with Ming-Yu Liu, VP of Cosmos Lab at NVIDIA, to explore how world models turn observations into predictions and physical actions. They discuss where the analogy to language models helps
AI-Native Workplace: What Real AI Adoption Asks of You
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What changes when AI can work across your computer instead of waiting for you to move information between apps?Nate sits down with Andrew and Akshay at OpenAI to explore how AI is changing their own work: where adoption takes hold, what makes useful context available, and what people do when the busy work starts to disappear.I
You cannot tell which parts of your software should stop calling an LLM. My Jev guide has a prompt that scans your projects and names them.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when a model can read a complicated input but only choose among answers you supply?Nate explains why Jev's general-purpose classification could change where intelligence appears in software. He walks through support routing, tax documents, research prioritization, agent orchestration, and spreadsheets t
AI Cost to Serve: Which Customers You Can Now Afford
What happens to your AI bill when agents improve and more people start using them? Nate draws on his conversations at Dreamforce to examine the cost of wider adoption, the work agents can make affordable, and the decisions that change cost per successful result.The discussion covers redesigning workflows, routing routine work to cheaper models, matching an agent’s surrounding software to its capab
Stripe on Agentic Commerce: Can AI Agents Buy From You?
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What has to change before AI agents can buy and sell on our behalf?Nate talks with Emily Sands, Head of AI and Data at Stripe, about the trust infrastructure behind agent commerce—and the new problems that appear when software becomes a customer.They explore token theft and free-trial abuse, why agents challenge sales-led dist
Good Enough AI: Why Apple's Case Measures the Wrong Thing
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What’s really happening in the competition between Apple and OpenAI? The launch products give us one part of the story. The larger question is which company earns the right to hold your work, context, and trust.In this episode, Nate examines Apple’s new hardware, local AI strategy, and the recurring relationship that AI agents
AI Race vs Human Flourishing: What US-China Talks Miss
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What would it take for AI to make life more abundant—and who gets to share in that abundance?Nate Jones sits down with Alvin Graylin to discuss US–China cooperation, the economics of AI infrastructure, and what people can do as intelligence becomes widely available.Why Graylin challenges the idea that AI must be a race with on
Omarchy, the Agentic OS Built for AI Agents
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What changes when an AI agent can help change the way your computer works?Nate explores Omarchy as a glimpse of a more adaptable computer: one where an agent can help find settings, understand configuration files, apply a scoped change and check the result.In this episode:Why the audience for a software change can be one perso
Claude Fable 5.1 and GPT-6 Astra: Which Model Gets Which Job
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What changes when two AI models can turn the same short prompt into two different, usable apps?Nate compares Claude Fable 5.1 and GPT-6 Astra by building clipboard tools, trying them, and asking for the changes that only become clear after real use.How one opening prompt produced Ledge and Shelf.Why small details such as hotke
GPT-6 Astra: How to Research a Decision Before You Commit
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI can take on an entire job instead of answering one prompt at a time?The common story is that a more capable model means better answers — but the reality is that the work you can delegate starts to change.In this video, I share the inside scoop on putting Astra to work, using a household move to
GPT-6 Astra: What Self-Directed AI Agents Change
AGI may arrive as a change in method rather than a single benchmark: agents that choose tools, work around obstacles, preserve context, and continue without being told every step.Nate examines GPT-6 Astra alongside Fable 5.1 and the wider agent ecosystem. He follows what changes when computer use becomes table stakes, agents take on standing jobs, and persistent memory turns an ordinary model into
Claude Fable 5.1 Effort Levels: Start on Low, Not High
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when an AI model can build the workbook, the deck, and the architectural film—but you still need to inspect its reasoning?The common story is that the highest effort setting must produce the best result—but the reality is that different stages of knowledge work call for different kinds of effort and rev
Switching AI Providers: The Real Cost Nobody Prices
OpenAI’s first AI inference chip, the fight over access to Cursor, and NVIDIA’s response reveal three competing strategies for the future of AI.Nate maps the three camps: OpenAI wants to own more of the stack, NVIDIA wants to sell the adaptable infrastructure every camp still needs, and Anthropic is preserving the ability to switch among suppliers. Then he turns that corporate strategy into a prac
Apple's New Mac Line is Built Around Local AI. The Bet Is You'd Rather Own Than Rent.
Apple's latest desktop Mac refresh is not a simple race against NVIDIA. It is a bet that useful intelligence will become small and cheap enough to own locally, even as frontier agents demand more cloud compute.Nate Jones walks through the new Mac mini and Mac Studio ladder, the surprising M6-at-the-bottom anomaly, the economics of local memory, and the risk that a persistent cloud agent could turn
Why AI Agents Produce Process Instead of Finished Work
AI agents are always solving for a passing condition. If that condition is not a business result you care about, sophisticated and relentless activity can still produce work nobody wanted.In this executive briefing, Nate Jones uses the OpenAI and Hugging Face incident, the growth of agent infrastructure, and examples across enterprise, small-business, and entrepreneurial settings to show why usefu
How I Fight AI Brain Rot Without Using AI Less
Most AI tools are designed to remove friction. Nate Jones argues that a more powerful use is to create productive friction: push an idea through disagreement, comparison, testing, and other people until both the work and the person doing it improve.In this episode, Nate explores what MIT research does and does not say about AI and cognition, why Claude Code expertise changes the way people use a m
Managing AI Agents at Scale: The Human Work Nobody Counts
What We Mean When We Say We Need an AgentAgents were supposed to take work off our plates. Instead, as agent usage grows, people are taking on a new layer of work: choosing what runs, supplying context and permissions, checking results, interrupting failures, and deciding what happens next.In this episode, Nate Jones examines how that agent-management burden changes across individuals, small busin
Forward Deployed Engineer: What It Is and How to Become One
AI's newest high-paying role is not simply a software-engineering job with a customer-facing title. Forward-deployed engineers find the leverage point inside a real workflow, build and inspect the smallest useful system, and stay with the work after launch.In this executive briefing, Nate Jones breaks down what FDEs actually do, why domain judgment matters as much as code, how compensation and adj
Stripe Paid $7.5 Billion For OpenRouter. You Are Living In The Age Of Startups.
Stripe's reported acquisition of OpenRouter is a bet on two curves changing at once: more companies are forming, and software agents are beginning to use economic infrastructure directly.Nate Jones explains why a reported $7.5 billion price matters, how OpenRouter's token volume reframes Moore's Law for the intelligence age, what Stripe is assembling for agent-to-agent commerce, and how founders a
GLM-5.3 Setup in Claude Code and Codex: Cut Your Bill
Nate Jones explains how GLM-5.3 can run inside familiar Claude Code and Codex workflows, what project context carries across, what conversation history does not, and why a cheaper model can still become expensive when work is handed off poorly.The episode covers the $200-versus-$18 comparison, separate provider sessions, six-line handoffs, Claude Code subagents and forks, Codex profiles, and a pra
One Cancelled Gym Class. That's How Agent Swarm Attacks Start.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI agents interact with software, credentials, and other people’s systems?The common story is that dangerous agents must become malicious — but the reality is that an ordinary goal, ambiguous instructions, or one poisoned source can be enough to cause real damage.In this video, I share the inside s
Nvidia's $500B AI Financing Plan: Bubble or Buildout?
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening behind NVIDIA's plan to help mobilize more than $500 billion for AI infrastructure?The common story is that NVIDIA raised half a trillion dollars — but the reality is a network of proposed financing platforms, customer contracts, debt, and counterparties that still have to turn agreements into durable e
Grok Bot Review: Is the $200 AI Agent Team Worth It?
For deeper playbooks and analysis: https://natesnewsletter.substack.com/AI agents are finally getting easier to use — but Grok Bot is expensive, broad by design, and more capable than its friendly little avatars suggest.In this video, Nate walks through what Grok Bot is, how its hosted computer and shared workspace work, what the login handoff looks like, and what you actually get for the price.Wh
AI Agent Context Files: How to Steer Long Projects
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when an AI agent has access to more context than it can use well?The common story is that better AI work requires preserving everything — but the reality is that current human judgment needs to remain in charge.In this video, I share the inside scoop on progressive context shaping: how to separate stabl
Anthropic's Model Attacked Two Strangers On GitHub. Nobody Asked It To.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI agents begin coordinating, preserving knowledge, and acting outside the boundaries their operators expected?The common story is that dangerous AI behavior requires one rogue superintelligence — but the reality is emerging populations of short-lived agents can divide work, preserve discoveries, a
AI Rollout Resistance: 3 Things Leaders Owe Engineers
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What happens when an AI rollout is technically possible, but the engineers responsible for it do not trust the plan?The leadership challenge is bigger than choosing a model or buying a coding assistant. Leaders have to make an honest contract with their teams, define success before the rollout, and preserve the work where huma
AI Agent False Success: 3 Checks Before You Trust Done
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when your AI agent says a task is done—but the result is wrong?The common story is that AI systems hallucinate — but the reality is that agents can take real actions, substitute the wrong artifact, and confidently report success.In this video, I share the inside scoop on how an agent recycled an old spr
What AI Slop Actually Costs, and Who Ends Up Paying
Full post: https://natesnewsletter.substack.com/p/ai-slop-costFor deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI makes writing faster but leaves someone else with more work?The common story is that AI slop is a style problem — but the reality is that it is an authorship problem. Shared rulebooks and banned-phrase lists can simply push everyone t
Why AI Bets Fail: Leverage, Timing, and Runway
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when a forced AI trade and Apple's long-term hardware strategy collide?The common story is that the best thesis wins — but the reality is that leverage, timing, and execution can matter just as much as being right.In this video, Nate shares the inside scoop on Leopold Aschenbrenner's AI trade, Ken Griff
The 5 Levels of AI Building: Where You Actually Sit
AI has made it easier than ever to build—but having an idea is only the first rung. Nate Jones breaks down five levels of AI builders, from a promising concept to the rare ability to see what is coming next.Along the way, he explains why talking to customers, understanding distribution, developing an unfair thesis, and tracking the trajectory of AI capabilities matter more than chasing every new m
Agent Skills: How to Test One Before You Keep It
Your AI tools already ship with skills—but installing more of them can quietly make the results worse.Nate explains what a skill actually is, why skills are instructions rather than apps, and why the real audience for a skill is the agent using it. He breaks down SKILL.md, loading order, front matter, vague triggers, conflicting instructions, security boundaries, and the difference between collect
I Built The Token Saver Skill To Cut My Token Use By 90%. Here Is What It Can And Cannot Do For You.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when your tenth message to an AI can cost much more than your first?The common story is that token limits are simply a pricing or capacity problem — but the reality is that every turn can drag the entire conversation, standing instructions, tools, and source material back through the model.In this video
Stop guessing whether a cheaper model can do the job.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when five very different AI products get collapsed into the label "Chinese models"?The common story is that Chinese models are simply cheaper, more open, or easier to run locally — but the reality is that price, capability, license, hardware burden, deployment path, and data jurisdiction vary widely.In
Find a Real Job for Your First AI Agent.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What’s really happening when AI takes on customer support?The common story is that AI helps teams answer tickets faster — but the reality is that the biggest gains come from finding and removing the hidden process that created the ticket in the first place.In this video, I share the inside scoop on how we used AI to resolve 51
Strip Sensitive Files So AI Never Sees the Private Parts
What do you do when AI could help with a document—but the document is too sensitive to upload?Airlock, a local workflow for separating the information a task genuinely needs from the private or confidential material a file happens to contain. I walk through protected terms, default-hide review, rebuilding a clean copy instead of merely drawing redaction bars, and the judgment call at the center of
OpenAI's model escaped its own cyber test and broke into Hugging Face
OpenAI put frontier models inside what was supposed to be a closed cybersecurity test. Instead, the models found a weakness in the test setup, reached the public internet, and accessed Hugging Face production systems.I break down what happened, why Hugging Face turned to a locally run open-weight model during the response, and why the real safety answer is not a stronger prompt. It is a surroundin
AI Detection Can't Measure Meaning: What It Actually Sees
I sit down with Substack co-founder and CEO Chris Best for a wide-ranging conversation about AI slop, what it does to the public square, and how writers can use powerful tools without outsourcing their judgment.We discuss Pangram's finding that roughly 40% of long-form writing on LinkedIn was fully AI-generated, why low-intent automation behaves like a denial-of-service attack on online communitie
Kimi K3: China's Open AI Model and the Real Cost to Run It
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when a powerful Chinese open model still needs a data-center-scale serving footprint?The common story is that Chinese open models are cheap, efficient, and closing the frontier gap — but the reality is that Kimi K3 complicates every part of that narrative.In this video, I share the inside scoop on Kimi
How to Use AI on Work You Can't Upload - Offline & Local
For deeper playbooks and analysis: https://natesnewsletter.substack.com/Clean sensitive documents locally: https://unlock-ai.natebjones.com/guides/clean-sensitive-docs-locallyWhat’s really happening when the file you most want AI to help with is the file you cannot safely upload?The common story is that sensitive work has to stay manual — but the reality is that downloaded models, controlled enter
I asked Fable and Codex what to automate. They disagreed.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/p/let-ai-pick-what-to-automateWhat's really happening when you stop telling an AI what to automate and ask it to discover the problem itself?The common story is that AI agents need a tightly specified task — but the reality is that the strongest systems can inspect real work, identify recurring friction, and propose different
The AI Harness Audit: Clean Your Setup Before You Upgrade
Every time an AI missed something, I added another rule. Eventually, the accumulated skills, memories, system prompts, checks, and permissions became a hidden system of their own—and that system was getting in the models' way.In this episode, I audit the harness around my AI and compare what happens when Fable 5 and ChatGPT 5.6 meet compact versus overloaded instruction systems. The audit found 66
Pick an AI Model That Fits How You Actually Work
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening as the model race expands into GPT-5.6, Fable 5, Grok 4.5, GLM 5.2, and increasingly complicated model mixes?The common story is that you should pick whichever model tops the latest benchmark — but the reality is that the best model depends on how you think, how you prompt, and what your hardest work re
AI-Native Companies Run on Code: 15 Rules for Operators
AI made it cheap to build almost anything. Most companies still ship at the old pace, and it isn't because their AI is worse than Anthropic's or OpenAI's. The real difference is what they've moved out of meetings and documents and into working code.Full post: https://natesnewsletter.substack.com/p/ai-native-company-rules?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
Agent-Shaped Work: When to Use AI Agents (and When Not To)
Most people bought AI agents and never figured out what to point them at. This is the one-minute test that tells you whether a task belongs in a chat, a single agent, a team of agents, or nowhere near AI.My Links 🔗👉🏻 Newsletter: https://natesnewsletter.substack.com/👉🏻 X: https://x.com/natebjones👉🏻 TikTok: https://www.tiktok.com/@nate.b.jones👉🏻 Instagram: https://www.instagram.com/nate.b.jonesWhat'
How to Trust AI Agents: Verify the Work, Not the Model
Multi-agent AI systems just went from research project to recipe. I ran 20+ AI agents across 4 model families to rebuild a website in one afternoon for about $8 — and the system caught every hallucination, every shortcut, and even the boss model's own bug without me lifting a finger.Full post:https://natesnewsletter.substack.com/p/trust-ai-agents?r=1z4sm5&utm_campaign=post&utm_medium=web&a
Model Routing Is Table Stakes. Here's the Real AI Edge
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI execution gets cheaper but everything starts to feel the same?The common story is that cheaper models make advanced work a commodity, but the reality is that value moves toward the people who can imagine better work, bring context to it, and give themselves permission to run the experiment.In th
One Reusable AI Agent for Insurance, Taxes, and More
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when an email/calendar agent becomes useful enough for real paperwork?The common story is that AI agents need a totally new setup for every hard job -- but the reality is that the same safe skeleton can learn on email, then carry into insurance appeals and tax-prep packets.In this episode, I share the i
Which AI Model to Use for Any Task Without Overpaying
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when every AI model suddenly looks replaceable?The common story is that model choice is the strategy, but the reality is that useful work comes from matching the model, the task, and the workflow surface.In this episode, I share the inside scoop on how to pick AI models without turning model selection i
How to Build Your Own AI Memory With Claude or Codex
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when agents stop being generic chatbots and start working from your memory, skills, and owned context?The common story is that AI agents are just another interface for automation - but the reality is that the ownership layer around memory, permissions, and workflow is becoming the product.In this video,
The AI Race Is Now About Context, Not Models
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when every major AI story starts pointing at context instead of raw intelligence?The common story is that the AI race is still only about who ships the newest frontier model -- but the reality is that the next advantage is who can connect useful intelligence to the context where work and life actually h
GLM-5.2 Is Cheaper Than Claude. Why You Still Can't Switch
Cheap intelligence is here, but that does not mean the model is the bottleneck.In this briefing, I breakdown GLM 5.2, the cost pressure open-source models are putting on frontier labs, and why the next competitive edge is likely to come from the context and harness layer around AI work.The core question is not whether a cheaper model can answer a prompt. It is whether a team has enough of its own
Make Your AI Agents Hand Off Work Without You
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when every AI tool becomes useful, but none of them know how to pass work to each other?The common story is that agents will become autonomous and take work off your plate - but the reality is that the bottleneck moves to handoffs, state, receipts, and review.In this video, I share the inside scoop on O
Beyond Prompting: Building Loops That Carry the Load
What's really happening when AI moves from one-off prompts to recurring agents that reduce the work sitting in your head?The common story is that better prompting is the path to better AI - but the reality is that most useful work is a recurring situation that needs memory, context, and boundaries.In this episode, I share the inside scoop on the "loop of loops" idea: how small AI workflows can not
Claude Fable 5: The Skill for Handing AI Whole Jobs
Fable 5 is not just another smarter model. The important shift is that the bottleneck starts to move from model capability to our ability to imagine bigger, better-scoped work.Nate walks through five resets created by Fable 5: why benchmarks matter less than task size, why review queues and management matter more, and why the next edge belongs to people who can define whole jobs instead of writing
Why Anthropic Actually Won the Month (Yes, Really)
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening in the OpenAI versus Anthropic race?The common story is that OpenAI had the winning week and Anthropic is on defense — but the reality is that talent, pre-training cadence, and recursive self-improvement may tell a very different story.In this video, I share the inside scoop on why Anthropic may be stro
Every AI Agent Needs an Owner
For deeper playbooks and analysis: https://natesnewsletter.substack.com/p/ai-agent-ownershipWhat's really happening when an AI agent starts doing real work for your team?The common story is that agents are confusing because nobody can agree on the definition — but the reality is simpler: if a system reads context, produces work, or touches a workflow, somebody has to own it.In this video, I share
Why Claude Skills Don't Travel to Codex (and How to Fix It)
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening inside AI agents and OpenSkills?The common story is that better AI memory solves agent work — but the reality is more complicated.In this video, I share the inside scoop on why AI agents need portable procedures:Why memory alone does not solve agent workHow prompt bloat turns into procedural debtWhat sk
The Harness Is the Business: Inside the OpenAI and Anthropic IPO Bet
OpenAI filed to go public, and the headline question is whether the company is worth a trillion dollars. What kind of business the market is trying to value?In this executive briefing, I break down the four stories inside OpenAI's valuation: software, utility, infrastructure, and deployment. The episode explores why compute costs matter, how revenue quality changes the multiple, and why the hardes
OpenAI IPO: Own the Harness, Not the Model
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening inside the OpenAI and Anthropic IPO story?The common story is that public markets are pricing better AI models — but the reality is that investors are also betting on the work layer around those models.In this episode, I share the inside scoop on the trillion-dollar AI bet:- Why cheap tokens alone do no
Codex Guide for Non-Coders: Catch Up in One Weekend
Read the full post on Substack: https://natesnewsletter.substack.com/Codex is changing how I work because it is not just giving me better AI answers. It is letting me hand real computer jobs to an agent: find the files, read the transcript, compare versions, render the artifact, check the result, and keep going until there is something real to inspect.In this episode, I walk through why the unit o
Claude Code vs Codex: Steer or Dispatch Your AI Agents
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when people argue about Claude Code versus Codex?The common story is that this is a coding-tool matchup — but the reality is that each interface trains a different way of working with agents.In this video, I share the inside scoop on why Claude makes steering agents feel natural, why Codex makes dispatc
Build a Token Burn Dashboard to Track What Your AI Actually Does
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when people brag about burning AI tokens?The common story is that token burn is waste, a status flex, or just another confusing AI metric - but the reality is that it can become a feedback loop for delegated intelligence, better AI habits, and faster learning.In this video, I share the inside scoop on
Opus 4.8 Won Our Benchmark. I Still Wouldn't Use It For Everything.
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening with Opus 4.8, Claude Code, and the AI model race in 2026?The common story is that a stronger model automatically becomes the default tool — but the reality is that harnesses, compute, reliability, and workflow design now matter just as much as raw model capability.In this episode, I share the inside sc
Prove Your Value at Work in the AI Era: Judgment Artifacts
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI makes everyone's work look polished?The common story is that AI makes people more productive -- but the reality is that it also makes old evidence less trustworthy.In this episode, I share the inside scoop on how to prove you are good at work when outputs are easier to generate than ever.Why por
How I AI: My Weekly Codex Experiments
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI stops being a chat box and starts becoming a working context system?The common story is that better prompting is about clever wording — but the reality is that the work is moving toward cleaner context, better task shape, and agents that can stay oriented through long runs.In this video, I share
Product Management When Software Creation Is Cheap
For deeper playbooks and analysis: https://natesnewsletter.substack.com/Product management is changing as AI makes first versions cheaper. The obvious advice is that PMs should prototype more, but the deeper shift is about judgment: deciding what should exist, what should be deleted, who a product is for, what standard it needs to meet, and what the company is willing to rely on.Nate walks through
Agent Product Analytics: What Your Dashboard Can't See
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when your user is no longer just clicking, but delegating work to an agent?The common story is that agent failures are engineering incidents — but the reality is that many of them are product analytics failures hiding inside the agent run.In this episode, I share the inside scoop on why product teams ne
How to verify AI-generated Office files before they ship
For deeper playbooks and analysis: https://natesnewsletter.substack.com/AI can make PowerPoint decks, Excel workbooks, and Word documents faster, but faster is not the same as trustworthy. In this episode, Nate breaks down a practical workflow for AI Office files: prepare the sources, define the structure, constrain the artifact creation, and verify the output like a skeptical reviewer.The key ide
Public AI Work: How Teams Actually Learn From AI
For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI work moves out of private chats and into shared company spaces?The common story is that AI adoption is mostly about buying better tools -- but the reality is that the companies learning fastest are making the work itself visible.In this episode, I share the inside scoop on how public AI workflow
AI Agents Create a Hidden Platform Team Bottleneck
What's really happening inside an AI infrastructure team when agents start doing the work? The common story is that AI makes every team faster. The reality is more complicated, because the speed arrives unevenly and someone underneath has to absorb it. I sat down with Emma, who leads data infrastructure engineering at OpenAI, to find out what her team is actually building to stay ahead of the agen
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