AI News & Strategy Daily with Nate B. Jones

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 stronger than the headlines suggest, and why the most important AI story may be happening outside the model labs entirely.Why the obvious OpenAI victory narrative is incomplete How Anthropic's pre-trained model position changes the race What talent movement says about recursive self-improvement Why Midjourney's medical imaging bet matters Where AI energy is moving beyond OpenAI and AnthropicFor builders, operators, and AI strategists, the shift is not just who wins the model horse race. It is where intelligence, capital, and applied products start compounding into new categories.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/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 the inside scoop on why every useful agent needs an owner, an operating loop, and a simple registry before it becomes part of real team work.Why agent ownership matters more than agent vocabulary How to tell when an assistant interaction has become agent work What an owner card should track before an agent affects a team Where review loops, permissions, and maintenance fit into the workflow Why maintenance is becoming the grown-up AI skill for 2026.This matters for operators, product leaders, builders, and executives because agent adoption is shifting from demos to durable workflows. The team that wins is not the one with the most agents; it is the one that knows what each agent does, what it reads, who reviews it, and who is accountable when it drifts.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 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 skills and runbooks actually make reusableWhere verification becomes the real quality barFor operators, builders, and teams, the opportunity is real: AI agents get more useful when your context and your procedures can move with you. OpenBrain gives agents the context; OpenSkills gives them the repeatable way to work.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.

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 hardest part of the AI market may be installing intelligence inside real organizations.Hosted on Acast.

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 not capture the value- How harnesses turn raw intelligence into real work- What forward-deployed engineering reveals about deployment- Where companies should own the AI work layerFor operators, builders, and executives, the takeaway is direct: using AI tools is useful, but durable leverage comes from owning the harness around the model.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.

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.