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AI News & Strategy Daily with Nate B. Jones
AI Data Center Backlash: Why It Helps Big Tech
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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.
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OpenAI's Git Backend for AI Agents: Vijaye Raji Interview
46:27|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.AI Agent Cost: How to Tell What the Work Is Worth
16:07|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.Zuckerberg's Second Swing: Lessons From Meta's Muse
22:26|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.Enterprise AI Agent Platforms: 8 Questions to Ask Vendors
29:20|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.Websites Are Blocking AI Agents. Pick One That Gets Through Meta
28:22|For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening as AI agents try to act on our behalf across the internet?The common story is that smarter assistants will do everything for us — but the reality is that websites and businesses are deciding which agents can enter and what they can do.In this video, I share the inside scoop on the trust problem shaping the agentic internet.Why useful personal assistants still get stopped at booking pages.How prompt injection and uncertain authorization complicate access.What open protocols and trusted intermediaries could make possible.Where agents already deliver practical value today.For builders and operators, the opportunity is to make agent access safe and understandable for both businesses and the people who delegate to them.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.Anthropic IPO S-1: Customer Growth, Margins, and Compute Meta
32:30|For deeper playbooks and analysis: https://natesnewsletter.substack.com/What does Anthropic’s S-1 reveal about the market for intelligence after a company starts using AI?Nate examines the gap between winning a customer and becoming important to that customer. The filing offers evidence of expansion while leaving major questions about deployment costs, workflow complexity, and future growth.Why a Fortune 500 logo can still represent a small deployment.How falling task costs can expand demand for intelligence.What distribution channels and deployment work mean for growth.What the filing answers—and does not answer—about five questions raised in June.For operators and builders, the practical question is how useful AI becomes in recurring work, beyond the initial purchase of a toolSubscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.AI Agent Overload: Cut the Noise in Codex and Claude
18:50|Running more AI agents can look productive without moving the work forward. Nate explains how to give agents useful assignments, recognize what deserves your attention, and stop recurring jobs once their purpose has passed.The practical test is whether the results help you make a decision or solve the central problem. From checking a painting estimate to questioning a weekly update nobody reads, the examples show why more output is not always more progress.SaaS Pricing Won't Tell You Your AI ROI
22:31|For deeper playbooks and analysis: https://natesnewsletter.substack.com/Nate explains why familiar seat and token pricing models leave companies responsible for turning intelligence into business value. Cedar's Kora example illustrates the difference between buying usage and paying for outcomes. A hypothetical equipment-service business shows how deployment knowledge and commercial partnerships shape the result.The practical question for leaders and employees is where to allocate intelligence: finish existing work faster, fix a recurring customer problem, or free people to pursue a revenue project that was previously out of reach.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.