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𝗧𝗛𝗘 𝟲𝟬-𝗦𝗘𝗖𝗢𝗡𝗗 𝗧𝗔𝗞𝗘

If I had to summarize this week in one sentence:

AI is moving out of the chat window and deeper into the actual work.

Google introduced reusable AI Skills inside Workspace.

ChatGPT moved directly into Microsoft Word.

Google pushed voice agents forward with Gemini 3.8 Live.

Apple finally put its new Siri AI into people’s hands.

Meanwhile, Microsoft, Anthropic, Salesforce and AWS gave us a clearer picture of what happens when companies move from experimenting with AI to actually deploying agents.

And there was an important warning mixed into all of it:

The more access we give AI, the more seriously we need to think about permissions, monitoring and human oversight.

Here are the stories I think matter most to entrepreneurs.

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𝗚𝗢𝗢𝗚𝗟𝗘 𝗪𝗢𝗥𝗞𝗦𝗣𝗔𝗖𝗘 𝗦𝗞𝗜𝗟𝗟𝗦 𝗠𝗔𝗬 𝗕𝗘 𝗔 𝗕𝗜𝗚𝗚𝗘𝗥 𝗗𝗘𝗔𝗟 𝗧𝗛𝗔𝗡 𝗔𝗡𝗢𝗧𝗛𝗘𝗥 𝗡𝗘𝗪 𝗠𝗢𝗗𝗘𝗟

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 5/5
Act now

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

On September 16, Google introduced Skills for Gemini inside Google Workspace.

A Skill can capture reusable instructions, rules, templates and reference files so Gemini can repeatedly perform a process according to the way your organization wants it done.

Skills can be created collaboratively and used across supported Workspace applications.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

This represents one of the biggest shifts happening in AI right now.

We are moving from:

“Write me a good prompt.”

to:

“Teach AI how our company does this job.”

That means brand standards, proposal processes, reporting procedures, customer-response frameworks and other repeatable knowledge can start becoming reusable organizational assets.

𝗪𝗵𝗼 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗺𝗼𝘀𝘁:

Almost any business already using Google Workspace.

𝗬𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗺𝗼𝘃𝗲:

Identify one job your team repeatedly explains to AI.

Document the instructions, examples and reference material required to do it correctly.

That process is a candidate for a reusable AI Skill.

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𝗖𝗛𝗔𝗧𝗚𝗣𝗧 𝗜𝗦 𝗡𝗢𝗪 𝗜𝗡𝗦𝗜𝗗𝗘 𝗠𝗜𝗖𝗥𝗢𝗦𝗢𝗙𝗧 𝗪𝗢𝗥𝗗

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 5/5
Worth testing

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

OpenAI released ChatGPT for Microsoft Word on September 17.

Users can draft from notes, summarize documents, revise selected text and change headings and formatting without leaving Word.

OpenAI also expanded the ability to connect multiple accounts to supported ChatGPT plugins.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

One of the biggest productivity killers in AI is still the copy-paste workflow.

Write something in ChatGPT.

Copy it into Word.

Notice something wrong.

Copy it back.

Fix it.

Paste it again.

Embedding AI directly into the document eliminates some of that friction and creates a much more natural AI-assisted workflow.

𝗪𝗵𝗼 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗺𝗼𝘀𝘁:

Consultants, agencies, coaches, lawyers, professional services firms and anyone producing proposals, reports, SOPs or client documents.

𝗬𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗺𝗼𝘃𝗲:

Pick one document you create regularly and test the Word integration against your current workflow.

Measure whether it actually reduces steps.

⸻

𝗚𝗘𝗠𝗜𝗡𝗜 𝟯.𝟴 𝗟𝗜𝗩𝗘 𝗣𝗨𝗦𝗛𝗘𝗦 𝗩𝗢𝗜𝗖𝗘 𝗔𝗚𝗘𝗡𝗧𝗦 𝗖𝗟𝗢𝗦𝗘𝗥 𝗧𝗢 𝗥𝗘𝗔𝗟 𝗪𝗢𝗥𝗞

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 5/5
Worth testing

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

Google introduced Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking on September 15.

The models are designed for near-real-time voice interaction.

Gemini can keep a conversation moving while performing tasks, working with visual context and using tools in the background.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

Voice AI gets dramatically more interesting when it stops being:

Ask question → wait → hear answer.

A useful voice agent needs to listen, reason, access information, take actions and continue the conversation naturally.

That opens possibilities for customer service, sales qualification, onboarding, training, scheduling and internal assistants.

𝗪𝗵𝗼 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗺𝗼𝘀𝘁:

Service businesses, sales teams, support teams, coaches, consultants and companies building voice-driven experiences.

𝗬𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗺𝗼𝘃𝗲:

Test it against a real conversation workflow rather than asking it trivia questions.

Try something like lead qualification, troubleshooting or onboarding.

⸻

𝗔𝗣𝗣𝗟𝗘 𝗙𝗜𝗡𝗔𝗟𝗟𝗬 𝗣𝗨𝗧 𝗦𝗜𝗥𝗜 𝗔𝗜 𝗜𝗡𝗧𝗢 𝗨𝗦𝗘𝗥𝗦’ 𝗛𝗔𝗡𝗗𝗦

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 5/5
Worth testing

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

On September 14, Apple began rolling out Siri AI and its next generation of Apple Intelligence.

The new Siri includes deeper personal context, broader knowledge, onscreen awareness and more system-level actions across Apple devices and applications.

Some server-side AI capabilities have usage limits, and Apple says expanded access will eventually be available for an additional fee.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

Millions of business owners already run huge parts of their businesses from iPhones and Macs.

AI embedded into the operating system may eventually be more important for mainstream adoption than another standalone AI app.

People won’t necessarily decide to “use AI.”

They’ll simply ask their phone to do something.

𝗬𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗺𝗼𝘃𝗲:

If you use Apple devices, test Siri AI against three real recurring tasks instead of treating it like the old Siri.

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𝗧𝗥𝗔𝗡𝗦𝗖𝗥𝗜𝗣𝗧𝗜𝗢𝗡 𝗝𝗨𝗦𝗧 𝗚𝗢𝗧 𝗖𝗛𝗘𝗔𝗣𝗘𝗥 𝗔𝗡𝗗 𝗣𝗢𝗧𝗘𝗡𝗧𝗜𝗔𝗟𝗟𝗬 𝗠𝗢𝗥𝗘 𝗔𝗖𝗖𝗨𝗥𝗔𝗧𝗘

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 5/5
Worth testing

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

SpaceXAI released Grok Voice Transcribe 2.0 on September 18.

The company says the new model is roughly twice as accurate as the previous version at the same price.

Batch transcription starts at about $0.10 per hour and streaming transcription at about $0.20 per hour.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

When transcription gets this inexpensive, audio becomes much easier to treat like structured business data.

Sales calls.

Customer interviews.

Meetings.

Podcasts.

Training sessions.

Support calls.

Videos.

All of them can feed AI workflows for summaries, research, CRM updates, content and customer insights.

𝗬𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗺𝗼𝘃𝗲:

Run one messy real-world recording through your current transcription system and Grok.

Compare accuracy and cleanup time, not just price.

⸻

𝗠𝗜𝗖𝗥𝗢𝗦𝗢𝗙𝗧 𝗦𝗧𝗨𝗗𝗜𝗘𝗗 𝟰𝟬,𝟬𝟬𝟬 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧𝗦. 𝗧𝗛𝗘 𝗣𝗔𝗧𝗧𝗘𝗥𝗡 𝗜𝗦 𝗪𝗢𝗥𝗧𝗛 𝗡𝗢𝗧𝗜𝗖𝗜𝗡𝗚.

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 4/5
Strategy signal

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

Microsoft analyzed usage patterns across more than 40,000 enterprise agents built with Copilot Studio.

Microsoft says the number of active agents in its Microsoft 365 ecosystem increased 15-fold over the previous year.

Productivity tasks remain the most common starting point, but companies are increasingly moving agents into operational work such as finance, security and supply chain.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

There is a lesson here for smaller businesses.

You do not need to start by building an autonomous AI employee.

Start with a narrow internal productivity problem.

Get it working.

Measure it.

Then expand into more consequential workflows.

𝗬𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗺𝗼𝘃𝗲:

Find one frequent task that wastes time but has relatively low consequences if AI makes a mistake.

That is usually a better first agent project.

⸻

𝗚𝗘𝗠𝗜𝗡𝗜 𝗔𝗖𝗖𝗘𝗦𝗦𝗘𝗗 𝗧𝗛𝗥𝗘𝗘 𝗥𝗘𝗔𝗟 𝗖𝗢𝗠𝗣𝗔𝗡𝗜𝗘𝗦 𝗗𝗨𝗥𝗜𝗡𝗚 𝗦𝗘𝗖𝗨𝗥𝗜𝗧𝗬 𝗧𝗘𝗦𝗧𝗜𝗡𝗚

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 5/5
Risk check

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

Google confirmed this week that Gemini unintentionally accessed systems belonging to three real companies during cybersecurity testing earlier this year.

The testing environment was supposed to simulate fictional targets but had unintended internet access.

In separate cases, the model guessed credentials or located publicly exposed credentials.

Google says the affected organizations were notified and the testing process was changed.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

The important lesson isn’t about Gemini.

It is about permissions.

If an agent has access to the internet, credentials, email, files, browsers and business systems, it may be capable of reaching much further than you intended.

The more capable agents become, the more important least-privilege access becomes.

𝗬𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗺𝗼𝘃𝗲:

Audit one agent.

Ask:

What can it access?

What can it change?

What can it publish?

What can it spend?

What requires human approval?

⸻

𝗔𝗡𝗧𝗛𝗥𝗢𝗣𝗜𝗖 𝗦𝗛𝗢𝗪𝗘𝗗 𝗨𝗦 𝗪𝗛𝗔𝗧 𝗛𝗜𝗚𝗛-𝗦𝗖𝗔𝗟𝗘 𝗔𝗜 𝗗𝗘𝗟𝗘𝗚𝗔𝗧𝗜𝗢𝗡 𝗟𝗢𝗢𝗞𝗦 𝗟𝗜𝗞𝗘

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 4/5
Strategy signal

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

Anthropic published internal measurements showing how much work it is delegating to Claude.

As of August:

Claude “leads” roughly 26% of measured AI research and development work.

More than 90% involves AI collaboration at some level.

But Anthropic reported no measured category operating fully autonomously.

On its largest internal research platform, roughly 30,000 agents can be active simultaneously, with agent actions passing through monitoring systems.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

This is a useful counterweight to all the talk about fully autonomous companies.

Even one of the world’s most advanced AI labs is combining aggressive delegation with heavy monitoring and human oversight.

That is probably a much better model for most businesses.

𝗬𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗺𝗼𝘃𝗲:

For one workflow, classify each step:

AI assists.

AI collaborates.

AI leads.

Human owns.

That simple exercise usually reveals where automation is safe and where oversight still matters.

⸻

𝗦𝗔𝗟𝗘𝗦𝗙𝗢𝗥𝗖𝗘 𝗜𝗦 𝗠𝗔𝗞𝗜𝗡𝗚 𝗜𝗧 𝗘𝗔𝗦𝗜𝗘𝗥 𝗧𝗢 𝗕𝗨𝗜𝗟𝗗 𝗔𝗜 𝗔𝗣𝗣𝗦 𝗔𝗡𝗗 𝗔𝗚𝗘𝗡𝗧𝗦 𝗪𝗜𝗧𝗛𝗢𝗨𝗧 𝗖𝗢𝗗𝗘

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 4/5
Agency opportunity

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

At Dreamforce, Salesforce announced Builder Central, Quick Pages and Agentforce Coworker.

Builder Central lets users describe applications and agents in natural language.

Quick Pages can create single-page applications on top of Salesforce data.

Agentforce Coworker provides an autonomous assistant inside Salesforce that operates within the logged-in user’s permissions.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

The technical barrier to building internal software continues to fall.

That changes where the value moves.

Knowing how to define the process, connect the data, establish permissions and test the workflow becomes more valuable than simply knowing how to build the interface.

𝗪𝗵𝗼 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗺𝗼𝘀𝘁:

Salesforce customers, CRM consultants, agencies and operations teams.

⸻

𝗤𝗪𝗘𝗡 𝗝𝗨𝗦𝗧 𝗣𝗨𝗧 𝗧𝗘𝗫𝗧, 𝗜𝗠𝗔𝗚𝗘, 𝗔𝗨𝗗𝗜𝗢 𝗔𝗡𝗗 𝗩𝗜𝗗𝗘𝗢 𝗜𝗡𝗧𝗢 𝗢𝗡𝗘 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗠𝗢𝗗𝗘𝗟

𝗦𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗰𝗮𝗿𝗲? 4/5
Advanced

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

Qwen released Qwen3.8-Omni-Flash on September 18.

The model can natively accept text, images, audio and video, supports context windows up to one million tokens and is designed for agentic tasks involving multiple media types.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀:

AI workflows increasingly involve more than documents.

A future agent may need to:

Read the proposal.

Watch the customer video.

Listen to the call.

Inspect the screenshots.

Then produce the deliverable.

Models designed around multiple media types make that kind of workflow increasingly practical.

𝗬𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗺𝗼𝘃𝗲:

Don’t change models just because a new one launched.

But if you have a workflow involving several media formats, add Qwen3.8-Omni-Flash to your next benchmark.

𝗧𝗛𝗜𝗦 𝗪𝗘𝗘𝗞’𝗦 𝗕𝗘𝗦𝗧 𝗔𝗜 𝗠𝗢𝗩𝗘

𝗕𝗲𝘀𝘁 𝗺𝗼𝘃𝗲:

Stop rebuilding the same prompt every time you do the same job.

𝗛𝗼𝘄 𝘁𝗼 𝗱𝗼 𝗶𝘁:

1️⃣ Pick one task you perform at least weekly.

2️⃣ Collect the instructions, examples, templates and reference material needed to do it correctly.

3️⃣ Turn that information into reusable AI context: a Skill, Project, Plugin, agent instruction set or equivalent system in the platform you use.

𝗘𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝗯𝗲𝗻𝗲𝗳𝗶𝘁:

Less repeated prompting, more consistent output and a much stronger foundation for eventual automation.

𝗔𝗜 𝗧𝗘𝗥𝗠, 𝗘𝗫𝗣𝗟𝗔𝗜𝗡𝗘𝗗

𝗥𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝗔𝗜 𝗦𝗸𝗶𝗹𝗹:

A reusable AI Skill packages the instructions, expertise, rules and sometimes tools required to perform a specific job.

Instead of teaching the AI how to do the job every time, you teach it once and reuse that knowledge.

Think of it as moving from:

“Here is my prompt.”

to:

“Here is how we do this job.”

𝗧𝗛𝗘 𝗥𝗘𝗔𝗟𝗜𝗧𝗬 𝗖𝗛𝗘𝗖𝗞

𝗗𝗼𝗻’𝘁 𝗰𝗵𝗮𝘀𝗲 𝘁𝗵𝗶𝘀 𝘆𝗲𝘁:

The fully autonomous AI employee that has access to everything.

𝗪𝗵𝘆:

The companies operating at the frontier are giving AI more responsibility, but they are also building monitoring, permission systems and escalation procedures around it.

Capability without control is not an automation strategy.

𝗪𝗛𝗔𝗧 𝗧𝗢 𝗪𝗔𝗧𝗖𝗛 𝗡𝗘𝗫𝗧

• OpenAI is preparing the transition from Custom GPTs to Plugins. Current guidance says Custom GPTs are scheduled for retirement December 11, with migration access rolling out separately by plan and workspace.

• Amazon introduced a new Bedrock AgentCore runtime designed to make long-running agents more elastic and better aligned with actual compute usage. Watch agent infrastructure economics as more businesses move agents into production.

• Kimi K3 arrived on Amazon Bedrock with native vision, a one-million-token context window and prompt caching. The interesting question is whether models such as Kimi and Qwen start materially changing the economics of document-heavy and agentic workloads.

Some people are treating AI privacy as an all-or-nothing decision.

It isn’t.

Here are the top 16 Ways I’ve Used Instinct Without Sharing Sensitive Personal Information

You can get real value from an AI assistant without handing it passwords, payment details, private client files, health records, confidential contracts, or anything else that could materially harm you if exposed.

Here are 16 ways I’ve actually used Instinct using public information, creative inputs, non-sensitive business context, or limited personal details I considered proportionate to the task.

1. Researched every Funnel Hacking Live speaker

I shared the public speaker page. Instinct built a briefing book with executive summaries, achievements, useful frameworks, questions to ask, public rapport facts, and sources.

Privacy posture: public speaker pages, interviews, podcasts, and other public sources.

2. Built the FHL schedule

Instinct pulled the official public schedule and organized all 17 event blocks in Las Vegas time.

Privacy posture: public event information. No sensitive personal data was needed.

3. Researched a coaching bootcamp before I bought it

It checked the public offer, pricing, curriculum, outside feedback, refund terms, and tier differences. When my screenshots proved transcripts were VIP-only, it corrected its recommendation.

Privacy posture: public sales material and screenshots of the offer, not credentials or payment data.

4. Investigated WebinarCon replay options

It checked the site, checkout, FAQ, refund terms, previous replay offers, and organizer posts. It found no current replay-only offer and advised me not to pay $1,999 just for recordings.

Privacy posture: public-source purchase research.

5. Drafted the exact WebinarCon question

When public research could not settle the replay question, Instinct wrote a concise inquiry covering price, included sessions, release timing, access duration, downloads, and deadlines.

Privacy posture: the draft disclosed only that I could not attend.

6. Helped during an airline disruption

I shared my route, flight details, seat situation, and travel constraint. Instinct tracked the inbound aircraft, found alternate routes, verified downgrade rights, and reduced a chaotic situation to a prioritized request for the gate agent.

Privacy posture: itinerary details are personal information, but I consider them low sensitivity and proportionate to the value. I did not share passwords, card details, or identity documents.

7. Researched a public marketing system

I asked it to study a UgenticAI speakers approach and assess what WBS could learn from it.

Privacy posture: public material plus ordinary business context.

8. Turned a fictional character into a short ad

I gave it the AI Hype Bro concept and asked for a 30-second script that made exaggerated AI job-loss fear look ridiculous.

Privacy posture: creative work using fictional material.

9. Turned the same idea into a comic

Instinct developed the concept visually and revised it into a cleaner grid after my feedback.

Privacy posture: supplied artwork and public-facing messaging.

10. Built a fact-based campaign about AI in education

It researched school AI bans, weak cheating arguments, international comparisons, and false positives from AI detectors.

Privacy posture: public policy, reporting, and published research. No student records.

11. Identified the real audience for that campaign

It helped move the strategy beyond educators toward parents and grandparents who can question school policy.

Privacy posture: public advocacy strategy, not private family or school data.

12. Pressure-tested a viral AI content idea

I shared an idea from a dinner conversation. Instinct challenged my assumptions and developed recurring concepts such as "Viral AI Claim: True or Trash?"

Privacy posture: I shared the idea, not private details about the people at dinner.

13. Applied a public business framework to WBS

I pasted a public post about generating more revenue with less effort. Instinct applied its risk, effort, and options filter to the AI Insiders conversion bottleneck.

Privacy posture: high-level business context. No customer list, payroll, financial statements, or private contracts.

14. Extracted and organized books from public material

Instinct identified and categorized 116 books from an official influencers free lead magnet guide and returned usable Markdown and CSV files.

Privacy posture: public source material.

15. Fact-checked a breaking news claim

I asked about reports involving a restrained airline passenger and a writer. Instinct separated verified identity from unproven claims about motive.

Privacy posture: public news research.

16. Audited criticism about Instinct itself

I asked it to assess The Atlantic’s security concerns, separate reported control failures from demonstrated exploits, and help me develop practical guardrails.

Privacy posture: public journalism, policies, and safer-use practices.

The point is not that privacy does not matter.

The point is that "AI can create privacy risk" does not mean "every useful AI task requires sensitive data."

A lot of the value comes from giving it a public link, a public question, a creative idea, non-sensitive business context, or limited personal details that are proportionate to the task.

Use judgment. Keep sensitive information out when the task does not need it. Put hard approval gates around communication, money, bookings, cancellations, and destructive actions.

But do not confuse the need for guardrails with a reason to avoid the tool.

What things are you ask your AI tools to do that don’t involve privacy concerns?👇

If you are using one AI model for everything, you are probably asking it to do two very different jobs.

THINKING work and DOING work are not the same thing.

Strategic planning, consulting, diagnosing a problem, making recommendations and designing a workflow all require stronger reasoning.

That is where premier models belong.

They help you work through the messy part. The part where the answer is not obvious and the quality of the thinking matters.

But once the plan is clear, the job changes.

If the task has clear instructions, defined constraints and an expected output, a lower-cost capable model may be perfectly suitable for the implementation.

Notice the word capable.

Lower cost does not mean lower standards. You still need a model that can competently handle the work you assign to it.

I think about it like this:

You might use senior expertise to design the building.

That does not mean the senior architect needs to carry every brick.

The skilled crew still matters. They just do a different job.

The same principle applies to AI.

Use your strongest models where judgment, planning and complex reasoning matter most.

Then use appropriately capable models for clearly defined execution.

Do not assume a cheaper model can handle every task.

Do not assume a premier model is necessary for every task either.

Choose the model based on the job being performed.

That is the practical takeaway: stop asking one model to do everything. Route the work according to the level of thinking the work requires.

What AI work are you still paying a premium model to do that a lower-cost model could probably handle?

P.S. I am fully capable of overcomplicating a simple workflow. This is one of the ways I keep myself honest.