AI News & Strategy Daily with Nate B. Jones

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.

For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI work moves out of private chats and into shared company spaces?The common story is that AI adoption is mostly about buying better tools -- but the reality is that the companies learning fastest are making the work itself visible.In this episode, I share the inside scoop on how public AI workflows can become apprenticeship infrastructure for teams learning to work with agents.Why Slack is becoming a practical substrate for human-AI collaborationHow Shopify's River workflow makes agent work observableWhat most companies lose when AI work stays hidden in private windowsWhere senior operators should make non-sensitive AI work publicWhy constraints can turn AI use into shared learning instead of isolated productivityThis matters for operators, builders, executives, and team leads who need AI adoption to compound across the organization, not just live inside the habits of a few early adopters.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information.

What's really happening inside an AI infrastructure team when agents start doing the work? The common story is that AI makes every team faster. The reality is more complicated, because the speed arrives unevenly and someone underneath has to absorb it. I sat down with Emma, who leads data infrastructure engineering at OpenAI, to find out what her team is actually building to stay ahead of the agents.In this interview, I share the inside scoop on why platform teams become the bottleneck when AI agents scale across a company:- Why app teams and platform teams accelerate at completely different rates- How goal-directed agents start to feel adversarial without meaning to- What OpenAI's data platform team built to buy back time- Where a private eval suite fits into surviving constant model upgradesFor platform and infra engineers, this is the telegraph from the future: the pinch point is coming, and the teams that instrument the load now are the ones who stay standing.For deeper playbooks and analysis: https://natesnewsletter.substack.com/

What's really happening inside the AI supply chain that powers every model you use?The common story is that AI is a software business with a fancy backend. The reality is more complicated, and it changes how you should buy, budget, and contract for AI.In this podcast, I share the inside scoop on why your AI vendor contract is now a supply contract in everything but name: • Why "capacity constrained" points to memory and packaging, not GPUs • How hyperscaler CapEx reshapes every vendor agreement you sign • What questions belong in your next AI investment review • Where a single supply chain delay stops you from shipping AIFor operators and CFOs, the takeaway is sober: cheaper tokens are real and serving costs keep falling, but the industrial base underneath your AI strategy still demands supply assurance, utilization discipline, and contracts that account for allocation risk.Subscribe for daily AI strategy and news.For deeper playbooks and analysis: https://natesnewsletter.substack.com/

Now I have the full transcript. Building the deliverable.What's really happening when prestigious law firms file motions full of AI hallucinations?The common story is that better prompts prevent hallucinations — but the reality is more complicated.In this video, I share the inside scoop on the project room workflow that makes hallucinations structurally unlikely: • Why your first AI prompt should never be "do the thing" • How agents now walk folder trees and compare files cleanly • What artifacts make an agent's judgment visible and inspectable • Where most serious knowledge work breaks down before the draftOperators doing high-stakes knowledge work with AI agents need to shape the canvas before the writing starts, or they ship the same soft spots that landed Sullivan and Cromwell in front of a federal judge.Subscribe for daily AI strategy and news. For deeper playbooks and analysis: https://natesnewsletter.substack.com/

Now I have the full transcript. Building the deliverable.What's really happening inside prompting now that AI agents are 100x more powerful than six months ago? The common story is that prompt engineering is dead — but the reality is more complicated.In this podcast, I share the inside scoop on the AI Question Method and why heavy knowledge work with frontier models demands a new mental model:• Why prompt engineering is now table stakes, not a skill • How to treat AI like a senior partner, not a junior • What three question principles unlock agentic knowledge work • Where most users still prompt like it is 2025For operators and builders, the agentic shift is a real opportunity, but only if you evolve your prompting alongside the models and learn to ask sharper questions instead of issuing tasks.Subscribe for daily AI strategy and news. For deeper playbooks and analysis: https://natesnewsletter.substack.com/

What's really happening inside the AI agent stack as agents move into production? The common story is that OpenAI and Anthropic decide whether your agent ships — but the reality is more complicated.In this podcast, I share the inside scoop on the infrastructure companies quietly deciding whether AI agents reach production:Why runtime, identity, and data are the real control layersHow Cloudflare, Auth0, and Snowflake gate agent deploymentWhat separates a kill switch from telling the model to stopWhere Stripe and the card networks are racing on paymentsFor builders and operators, the agentic shift is a real opportunity, but only if you map runtime, identity, data, payments, and observability for each workflow before it ships, not after.Subscribe for daily AI strategy and news. For deeper playbooks and analysis: https://natesnewsletter.substack.com/

What's really happening inside the agent protocol stack as Google I/O kicks off? The common story is that every new protocol is a must-have standard — but the reality is more complicated.In this postcast, I share the inside scoop on the six agent protocols shaping how AI agents actually ship and how customers experience them:Why three protocols are becoming the real agent stackHow MCP, A2A, and AGUI map to core agent jobsWhat separates a standard from a contested protocolWhere payment protocols collide with customer trustFor builders and operators, the agentic substrate is a real lever on customer experience, but only if you stop chasing acronyms and start asking which protocols actually shape the workflow you're shipping.Subscribe for daily AI strategy and news. For deeper playbooks and analysis: https://natesnewsletter.substack.com/

What's really happening inside the AI-driven shift in marketing?The common story is that AI makes marketing faster — but the reality is that the entire internet economy is moving from attention to interpretation, and most marketers are still optimizing for the wrong one.In this video, I share the inside scoop on the two-internet economy and what it means for marketers and individuals: - Why AI agents now sit between buyers and brands in B2B and consumer - How a truth layer wins where emotional marketing copy fails with LLMs - What AI-washing costs companies and candidates trying to look AI-native - Where marketing has to touch — website, pricing, docs — to stay relevantThe marketers and candidates who win in 2026 will be the ones who build memory in humans and clarity for agents, not the ones automating the back office faster.Subscribe for daily AI strategy and news.For deeper playbooks and analysis: https://natesnewsletter.substack.com/