AI News & Strategy Daily with Nate B. Jones

Reverse Engineer Anything(REA) is more useful than a shortcut for copying apps. Nate explores how inspecting real software can reveal the requirements, relationships, and edge cases behind the interface—and help you explain what your own software should do.Examples include authenticator codes, Notion copy and paste, Windows Calculator percentages, accounting behavior, and game mechanics. The practical takeaway: investigate a behavior, predict its result, and build checks that show whether you understood it. Hosted on Acast. See acast.com/privacy for more information.

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.How a document-search feature can pass tests and still create a privacy problem.Why discovering the right question matters as much as generating an answer.What focus, experience, and the desire to make an impact still contribute.<...

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 learning from it.What sustained focus contributes when starting new projects gets easier.How resourcefulness helps you keep the intent while changing tools.Why shipping a first...

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 example, he asks who defines a task as done, what simulations can teach agents, and how workers and employers can preserve the context that makes work useful.Su...

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 everyday tasks, the next opportunity belongs to builders who understand their customers and use the products they sell.Subscribe for daily AI strategy and news.Hosted on...

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 software buyers, sellers, and individual team members can do about it.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.<...

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 models and higher subscription tiers against time saved and value created.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information. Hosted on...

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 why enterprise distribution matters, how source conflicts undermine answers, and why the price of a model is only part of the cost of getting a job done.Subscribe for daily AI strategy and news.<...

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 work, and a practical way to rerun your own assignments when a new model arrives.Subscribe for daily AI strategy and news.Hosted on...

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 shopping journey matters to Amazon and retailers.How personal context can shape competition between assistants.Nate’s savings figures describe future spending avoided, not a cash refund. Other...

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 agents reliable checks.Leave clear handoffs for the next session.Remove process that no longer helps.The goal is to improve the systems around the agents so...

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, where it breaks down, and why knowing whether an action worked can be harder than generating the action itself.How simulation lets builders test ma...

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.In this conversation:How access to the right context can change who uses AI at work.What it means to share...

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 that respond to meaning.Why complicated inputs and simple outputs define a useful class of problems.How classifiers, generative models, and ordinary code fit together.What lower classification costs make possible for teams and individual builders.Where...

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 capabilities, and evaluating results before scaling up.More analysis and practical playbooks: https://natesnewsletter.substack.com/Editorial note: Draft podcast copy; current Acast house format has not been in...

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 distribution, how Stripe is adapting its tools for agents, and the unsettled economics of AI pricing. The conversation moves from a practical question—would you trust AI to b...

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 could build with their users.Why selling the phone may not mean owning the most valuable customer relationshipHow cheap local compute can make room for...

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 one winner.How specialization and human judgment may change as AI improves.What infrastructure spending and corporate adoption reveal about the transition.Why...

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 person.What agents need to make useful changes to an operating system.How to match permissions to the task and keep...

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 hotkeys and copy confirmation change the experience.How faster iteration influenced Nate’s preference in this specific build.Why different models can reveal preferences you had not ye...

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 explore what an agent can prepare and which decisions still belong to you.Why connected tasks need mo...

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 something that knows a person or business over time.In this episode:Why “nobody told it how” is the key shiftWhat separates a superagent from a chatbotHow pers...

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 review.In this episode, I share the inside scoop on my Fable 5.1 tests: an acquisition model in Excel, an executive PowerPoint, a 100-word Toyota writing challenge, and...

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 practical personal decision: how to spend $20, $60, or $200+ per month without letting one provider control your memory, files, instructions, and work.In this episode:Wh...

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 the Mac into little more than an excellent terminal.In This EpisodeWhy Apple placed the newest M6 generation at the bottom...

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 useful agents need better finish lines.In This EpisodeWhy an agent's passing condition matters more than its activityWhat the 1,200-agent OpenAI incident reveals about incentivesThe ordinary-engineer test for maintainable agent-written...

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 model, how a convincing output can conceal the wrong source data, and why the point is not to become a meat puppet for AI....

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 businesses, and enterprises. The examples range from OpenRouter and Codex usage to Anthropic's Claude Code research, small-business AI spending, the PocketOS and Railway recovery story, and the emerging idea of...

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 adjacent titles vary, and a practical four-week plan for building the skill before anyone gives you the title.In This Episode· Why evals can be technical work even when t...

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 and incumbents should respond when their old base case stops behaving normally.In This EpisodeWhy Stripe paid a reported premium for OpenRouterThe 11-week token-doubling curveHow coding agents rediscovered Stripe's seven-year-old CLIThe emerging...

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 practical routing rule: give bounded, testable work to the cheaper model while keeping hidden-state investigations and risky judgment calls with the strongest model you trust.Prices and plan details are current as of August 2026...

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 scoop on the agent-security incidents that are beginning to connect:Why a gym-booking agent canceled a real person’s reservationHow poisoned skills can redirect already-trusted agents...

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 economics.In this video, I share the inside scoop on how AI infrastructure gets financed, why circular relationships are not the whole story, and what operators and investors should examine when the next gi...

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.Why Grok Bot feels simpler than self-hosted agent toolsHow one authorization can support multiple bots inside a shared environmentWhat the $200 monthly plan includes — and how...

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 stable instructions, current state, retrieval maps, and history so an agent can keep moving without stale decisions steering the work.Why giant instruction fi...

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, and become more capable as a group.In this episode, Nate breaks down OpenAI agents rebuilding a deleted message board, the UK AISI's real-world Mythos 5 incident, and the movement of elite Google researchers into recursive-improvement startups.<br...

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 human judgment still matters.In this episode, Nate lays out three principles for leading AI adoption without losing the people who have to make it work.<...

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 spreadsheet, why verifiable rewards can still produce false success, and how to build a stronger operating system around agent work.Why...

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 toward a different version of the same generic output.In this episode, Nate shares the inside scoop on why authorship matters in the age of AI:...

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 Griffin's forced-sale opportunity, and why Apple can remain a default winner no matter which AI lab leads.Why leverage can break a position without breaking th...

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 model release.This episode is a practical framework for finding your current rung and building toward the next one. Hosted on Acast. Se...

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 collecting skills and deliberately shaping them for your own workflow.The episode moves from a beginner-friendly definition to the advanced problem of auditing a stack of 20–25 skills. The practical takeaway: use...

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, I share the inside scoop on how to keep that AI desk clean and put more of your tokens toward useful work.Why re...

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 this video, I share the inside scoop on how I evaluate DeepSeek V4 Pro, Kimi K3, GLM 5.2, MiniMax M3, and Qwen.Why cheap tokens can still pr...

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 of 52 support issues in one week, reduce a comparable week from 52 cases to 19, and eliminate our largest recurring category.Wh...

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 safe AI work: start with the job, not the file.The episode also explores why this problem has become urgent as AI workflows absorb more real pr...

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 surrounding harness: a safe autopilot that limits the control surfaces available to an increasingly capable model.This episode also explores the refusal asymmetry facing defenders, trusted access...

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 communities, and what Substack is doing to add transparency without policing creators' tools.The conversation also covers thin versus thick wrappers around AI, proof of work, Claude-fishing, the future of video, and why human...

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 K3, Moonshot AI's coming open-weight release, and what the model says about the next stage of the AI race.Why 64 accelerator cores changes the me...

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 enterprise systems, and narrow specialist workflows now create several practical paths between “send it to a chatbot” and “do not use AI.”In this episode, I share the inside scoop on how...