AI News & Strategy Daily with Nate B. Jones

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 agent lying is different from chatbot hallucinationHow a second agent can review actions and tool callsWhat good supervision and harness work look likeWhy you should ask boldly and verify quicklyOperators, builders, marketers, and executives should care because the bottleneck is shifting from whether agents can act to whether their work can be trusted.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.

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:Why AI slop pushes work downstream instead of making it disappearHow model convergence produces the same hill-climbing behaviorWhy universal anti-slop checklists cannot create a distinctive voiceWhat a pro-authorship process looks like in practiceHow better drafts protect scarce human attentionFor operators, builders, marketers, and executives, the standard is simple: use AI to stay in the work—not to escape responsibility for 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/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 the thesisHow a margin call turns market pressure into a forced saleWhy Apple's chips make it valuable across competing AI ecosystemsWhat Apple still has to execute to turn position into strategyOperators, builders, investors, and anyone making long-horizon AI bets should care about the difference between having the right position and actually capitalizing on it.Subscribe for daily AI strategy and news.

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.

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 existing skills as raw material, then sharpen them around the work, preferences, and principles that are uniquely yours.Topics include:Skills as recipes for AI agentsWhy skills are not appsAgents as the audience and humans as readersName, description, front matter, and loading orderSecurity, permissions, and trustThe Pokémon-card trap of collecting skillsConflicts across a large skill stackBuilding and auditing skills for your own workflow

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 reused input compounds across a long conversationHow to select evidence and send the lightest useful sourceWhat the Token Saver skill handles automaticallyWhere prompt caching helps and where it does notHow a local gateway can constrain a request before the model callOperators, builders, and everyday knowledge workers should care because better models do not eliminate the need to manage context. The practical shift is to carry accepted results forward, keep source packets light, and stop paying repeatedly for work the model has already seen.Token Saver guide: https://unlock-ai.natebjones.com/guides/cut-token-wasteRinger guide: https://unlock-ai.natebjones.com/guides/ringerRelated reading: https://natesnewsletter.substack.com/p/context-windows-are-a-lie-the-mythSubscribe 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 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 produce expensive finished workHow open weights, usable licenses, and practical self-hosting differWhat "cost per accepted result" reveals that token price hidesWhere deployment, data path, and jurisdiction change the riskHow to run a 20-example bakeoff against your own real workOperators, builders, and executives should care because the right decision is not "Chinese model or American model." It is which job, which artifact, which deployment path, and which failure mode your organization can accept.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 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.Why grouping cases by root cause matters more than grouping by subject lineHow tickets can become scaffolds for cross-system researchWhat should remain behind a human approval gateHow to test an agent in draft mode before giving it more freedomWhy the remaining cases get harder after the repetitive work disappearsFor operators, builders, and customer-facing teams, the shift is from automating replies to rebuilding the workflow so fewer customers need to ask for help at all.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.

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 proposals, contracts, meeting notes, and code; what Verizon’s 2026 DBIR says about AI use on corporate devices; and why NIST’s idea of “security fatigue” helps explain the appeal of the fastest upload path.Key takeaway: useful AI context and sensitive information are often bundled together, but they are not the same thing.