AI News & Strategy Daily with Nate B. Jones

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 of work is changing, how my token dashboard became a receipt for agentic work, and why threads, goals, computer use, plugins, and skills matter for people who do knowledge work outside of code. The practical takeaway is simple: pick one annoying and valuable loop, give Codex sources, standards, boundaries, and proof, then learn how to verify what came back.

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 dispatching agents feel natural, and why the skill of 2026 is agent literacy.Why the Claude versus Codex question is usually framed wrong How agent tools teach habits, not just features What Claude is better for when the work is fuzzy Where Codex shines when the work can become a delegated job Why the human role becomes judgment, proof, and tasteIf you manage knowledge work, build with AI, lead teams, or just want to understand where agents are going, the shift is not only which model is smarter. The shift is what work you can now imagine assigning, reviewing, and trusting.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/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 building a token burn dashboard and what it taught me about using AI well.Why more agents and more tokens can lead to better answersHow a usage dashboard turns scattered work into a learning loopWhat top token days reveal about real AI fluencyWhere public charts and shared accountability make people better togetherWhy the next edge is not just using AI, but studying how you use itIf you are an operator, builder, marketer, executive, or anyone trying to get more value out of AI, the shift is simple: stop treating usage as a vanity metric and start treating it as evidence you can learn from.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/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 scoop on why Opus 4.8 is a strong but complicated release, why it is not automatically my daily driver, and why Codex currently fits certain long-running agent workflows better.Why Opus 4.8 reads more like a checkpoint release than the Mythos moment people expectedHow reasoning effort can become unpredictable when a model overthinksWhat a harness is, and why it now decides daily-driver behaviorWhy Claude Code's /workflows command is a real agent-pattern innovationWhere knowledge workers and engineering leaders should focus in the second half of 2026This matters for builders, executives, CTOs, CIOs, and operators trying to decide where to place AI budget. The practical question is not which model wins forever. It is how you architect your work so you can route tasks to the model and harness that best drive the outcome.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/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 portfolios are no longer enough on their ownHow whiteboard-style conversations reveal judgmentWhat situation, decision, risk, and change show about real workWhere Talent Board-style evidence fits into careers and hiringHow to make your reasoning visible without over-performingIf you hire, manage, build, or are trying to grow into a new role, the shift matters: the scarce signal is no longer just what you produced. It is whether people can see how you understood the problem, handled tradeoffs, and improved the work.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/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 the inside scoop on how I'm using AI this week: assembling context windows, using Codex on local files, and shifting from prompt engineering into collaborative task definition.Why local folders can become clean context windows How Codex changes long document, spreadsheet, and code workflows What changed in prompting after agentic workflows got better Where Claude still fits for polish, salience, and design Why multi-threaded drafting now feels practicalFor operators, builders, marketers, and executives, the important shift is not just which model wins. It's learning how to structure the work so the model can help you think, execute, review, and iterate.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/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 the move from rationing scarce engineering to classifying software abundance, including the Prototype Commons, production class ladders, and why promotion and demotion become core product work.Hosted on Acast. See acast.com/privacy for more information.

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 need a new analytics layer for agent products.Why chat logs are not enoughHow agent runs replace sessions as the unit of behaviorWhat Salesforce's Agent Work Units signal about SaaS metricsWhere completion, acceptance, and correction rates fitWhy product analytics becomes the rudder for agent autonomyOperators, product leaders, and builders should care because agents move too fast for old dashboards. If you cannot see intent, tool calls, permissions, corrections, completion, and trust in one run-level view, you are steering with missing instruments.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/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 idea: the file is not the whole thing. The file is the visible output of a knowledge-work system. If the claims, numbers, sources, assumptions, charts, and formulas cannot be traced, the artifact may look finished while quietly breaking trust.Hosted on Acast. See acast.com/privacy for more information.