AI News & Strategy Daily with Nate B. Jones

For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when AI stops being a chat box and starts becoming a working context system?The common story is that better prompting is about clever wording — but the reality is that the work is moving toward cleaner context, better task shape, and agents that can stay oriented through long runs.In this video, I share the inside scoop on how I'm using AI this week: assembling context windows, using Codex on local files, and shifting from prompt engineering into collaborative task definition.Why local folders can become clean context windows How Codex changes long document, spreadsheet, and code workflows What changed in prompting after agentic workflows got better Where Claude still fits for polish, salience, and design Why multi-threaded drafting now feels practicalFor operators, builders, marketers, and executives, the important shift is not just which model wins. It's learning how to structure the work so the model can help you think, execute, review, and iterate.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information. Hosted on Acast. See acast.com/privacy for more information.

For deeper playbooks and analysis: https://natesnewsletter.substack.com/What's really happening when your user is no longer just clicking, but delegating work to an agent?The common story is that agent failures are engineering incidents — but the reality is that many of them are product analytics failures hiding inside the agent run.In this episode, I share the inside scoop on why product teams need a new analytics layer for agent products.Why chat logs are not enoughHow agent runs replace sessions as the unit of behaviorWhat Salesforce's Agent Work Units signal about SaaS metricsWhere completion, acceptance, and correction rates fitWhy product analytics becomes the rudder for agent autonomyOperators, product leaders, and builders should care because agents move too fast for old dashboards. If you cannot see intent, tool calls, permissions, corrections, completion, and trust in one run-level view, you are steering with missing instruments.Subscribe for daily AI strategy and news.Hosted on Acast. See acast.com/privacy for more information. 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. Hosted on Acast. See acast.com/privacy for more information.

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/ Hosted on Acast. See acast.com/privacy for more information.

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/ Hosted on Acast. See acast.com/privacy for more information.

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/ Hosted on Acast. See acast.com/privacy for more information.

What's really happening inside AI investment decisions at most companies? The common story is that you need an AI strategy — but the reality is more complicated.In this video, I share the inside scoop on how to allocate capital across build, buy, hire, and wait for AI agents and workflows:Why workflow shape, not AI strategy, drives investmentHow to pick between automate, build, buy, hire, waitWhat separates a real AI hire from a unicornWhere most agentic AI projects quietly failFor operators and executives, the agentic era opens unprecedented upside, but only if you stop chasing a singular AI strategy and start making disciplined capital allocation decisions one workflow at a time.For deeper playbooks and analysis: https://natesnewsletter.substack.com/ Hosted on Acast. See acast.com/privacy for more information.

What's really happening inside SaaS pricing as AI agents take over the work? The common story is that agents will just replace seats — but the reality is more complicated.In this video, I share the inside scoop on how the agent era is rewriting SaaS economics and what to negotiate before your next renewal: • Why seat-based pricing is breaking under AI agents • How Salesforce, Microsoft, and ServiceNow meter agentic work • What separates a fair agent license from rent-seeking pricing • Where SAP-style API policies could lock out your agentsFor operators and builders, the agentic shift is a real opportunity, but only if you negotiate the meter, the caps, and the access path before usage gets embedded and your leverage disappears.Chapters:00:00 Agentforce hits $800M run rate00:55 Four questions before your next renewal01:45 Why the seat model is breaking02:50 Salesforce Flex Credits and work units03:40 Microsoft Copilot credits and hybrid pricing04:45 The 8 billion token developer story05:30 ServiceNow Action Fabric and operational metering06:30 SAP 2026 API policy and agent lock-out07:45 Pricing follows platform control08:40 Fair license versus rent-seeking patterns10:00 What builders must know about cost structure11:30 Negotiating agent access before usage embeds13:00 The commercial unit of software is changingSubscribe for daily AI strategy and news.For deeper playbooks and analysis: https://natesnewsletter.substack.com/ Hosted on Acast. See acast.com/privacy for more information.

What's really happening inside the AI agent memory infrastructure war?The common story is that bigger context windows and better vector search will solve it — but the reality is every serious infrastructure vendor is racing to fix a deeper problem that classic RAG can't touch.In this video, I share the inside scoop on why memory is now the real battleground for production AI agents: • Why classic RAG was built for chatbots, not agents • How Pinecone, PageIndex, SAP, and GraphRAG attack different shapes • What a retrieval contract actually looks like for AI agents • Where most agent builds quietly waste their token budgetBuilders who write down what their agent needs before picking a database will ship reliable systems — the ones who shop vendor-first will keep paying for rediscovery on every run.Subscribe for daily AI strategy and news.For deeper playbooks and analysis: https://natesnewsletter.substack.com/ Hosted on Acast. See acast.com/privacy for more information.

What's really happening when AI agents take real actions in production, and why do better prompts keep failing to stop them?The common story is that prompt engineering and human approval will keep AI agents safe — but the reality is that frontier-model agents now need their own manager: a separate LLM-as-judge that guards your intent at the action boundary.In this video, I share the inside scoop on the architectural pattern that's quietly replacing prompt-based guardrails in serious agentic systems: • Why prompts and manual approval both break under real agent workloads • How Lindy redesigned its system after agents started sending unauthorized emails • What the four action-risk classes mean for read, write, and high-stakes calls • Where correlated judgment fails and frontier models change the calculusBuilders shipping agents without a judge layer are gambling on every tool call — the teams who classify actions, instrument a four-way decision scope, and put a frontier model in the judge seat are the ones whose agents will actually be trusted to do real work.Subscribe for daily AI strategy and news.For deeper playbooks and analysis: https://natesnewsletter.substack.com/ Hosted on Acast. See acast.com/privacy for more information.

What's really happening with codex plugins, skills, prompts, and MCPs as agents start doing real work? The common story is that plugins are just app store add-ons — but the reality is more complicated.In this video, I share the inside scoop on the agentic scaffolding that actually makes AI useful: • Why prompts work for one-offs but break under repeated workflows • How skills encode your house style across any LLM you use • What plugins package up and why they're bigger than MCPs • Where hooks, scripts, and connectors fit inside the larger systemFor operators and builders, the leverage in 2026 lives in knowing which part of your workflow belongs in a prompt, a skill, a plugin, or an MCP — and packaging the right ones so your team can actually reuse them.Subscribe for daily AI strategy and news.For deeper playbooks and analysis: https://natesnewsletter.substack.com/ Hosted on Acast. See acast.com/privacy for more information.