AI News & Strategy Daily with Nate B. Jones

For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI keeps improving but customers have less reason to pay for the upgrade?The common story is that more capable models automatically create more revenue — but the reality is that buyers can test what is good enough and move routine work to cheaper models.In this video, I share the inside scoop on who gets paid as AI becomes more useful and the market for intelligence becomes more demanding.Why token use can rise while the bill stays the same.How evals turn model selection into a purchasing decision.What Ramp's spending data reveals about adoption and pricing power.Where productivity gains go when customers expect lower prices.What stays valuable as models improve and technical work becomes easier.For business leaders, the decision is about the full cost of useful work: model spend, review, repairs, and the surrounding systems. Set a baseline on real assignments, then ask what value your business can retain as customers and competitors adapt.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 stronger AI model still feels like an assistant?The common story is that more capability makes every task better — but the reality is that people still have to find problems where the improvement is worth the cost.In this video, I share the inside scoop on my early access to Fable 5.2 and the e-ink display project it helped finish after earlier attempts fell short.How the model inferred my design intent from failed attempts.Why a proactive handoff made the build easier.What task-level saturation means for everyday work.How to identify projects with enough value to justify expensive AI.Why judging solutions and communicating intent remain essential skills.For operators and builders, the useful question is where better AI can deliver a result worth paying for. Meanwhile, labs have a clear use for that intelligence: building the next model.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 Jensen Huang, chip access, and the pace of AI?The common story is that this is a CEO scandal — but the reality is that scarce computing power connects the NVIDIA investigation, AI-assisted research, and businesses' growing cyber-defense needs.In this video, I share the inside scoop on:Why Chinese buyers are waiting for NVIDIA chips.How overseas computing access complicates the story.What AI-assisted research could mean for the next generation of models.How an internal AI tool figured in the Binance attack.For operators and builders, chip availability shapes both what AI can do and who can use it. Nate distinguishes unresolved allegations from the reporting and considers what the next year could mean for business planning.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 Americans use more AI but reject new data centers?The common story is that the backlash will stop the AI buildout — but the reality is that demand keeps growing, and constraints change who can serve it and where investment goes.In this video, I share the inside scoop on the consequences beyond the ballot box:Why delays hit smaller developers hardest.How existing capacity can strengthen large technology companies.Where investment, jobs, and computing power may move next.What makes orbital compute a speculative alternative.Why the industry needs to connect AI with empowerment and opportunity.For operators, builders, and people using AI at work, construction headlines are only one signal. Watch what companies do with the next project and how they secure the capacity they need.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 changes when agents can write code faster than teams can review, test, and ship it?Nate Jones talks with Vijaye Raji, CTO of Applications at OpenAI, about how agents are changing work inside the company—from engineering infrastructure to finance and product development.Why code review, testing, and repositories become the next bottlenecks.How to decide when a human should review an agent's response.Why finance teams are sharing purpose-built sites instead of spreadsheets.What founders should ask when a more capable model arrives.How small teams use models to build and iterate faster.For builders and leaders, the conversation offers concrete ways to rethink workflows around improving models while keeping human judgment in the right places.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-agent-cost-worth-it?utm_source=podcast&utm_medium=podcast&utm_campaign=ai-agent-cost-worth-it-launch&utm_content=show-notesWhat is an AI agent’s work actually worth?An agent’s serving cost and the value it creates are different questions. In this episode, Nate looks at how to find work worth paying for: the meeting nobody can resolve, the report everyone inherits, and the internal idea that needs a small working prototype.The practical test is whether your agent creates clarity, saves meaningful effort, and produces a result you can point to. Include your own checking time and the full cost of the tools, then decide where more time or money makes sense.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.

Full post: https://natesnewsletter.substack.com/p/zuckerberg-muse-lessons?utm_source=podcast&utm_medium=podcast&utm_campaign=zuckerberg-muse-lessons-launch&utm_content=show-notesWhat can the rest of us learn from Zuckerberg’s next big bet?The common story is that Meta’s resources explain Muse. But the useful lessons are about the people behind the product, the details that make an assistant feel dependable, and the willingness to try again after a very public failure.In this episode, I look at:Finding the right people and giving them room to build.Why the bathroom tells you more than the entrance about care.The status details that make an AI assistant easier to trust.How AI changes the cost of trying without eliminating embarrassment.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/Why would Salesforce help Microsoft's agents, and why would Oracle bring its applications into ChatGPT? Nate examines the competition to coordinate work across the software a business already owns.How agent platforms change the relationship between enterprise software vendors.Why connecting data is different from establishing its business meaning.What an agentic control plane does, and who gets to define its rules.How teams and individual employees can advocate for interoperability when choosing AI tools.For leaders, builders, and people influencing AI purchases, today's tool decisions help determine who controls tomorrow's workflows.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.