So here’s a question I’ve been thinking about based on a The Economist article I recently read:

Would you run your entire company on electricity you rented from a single supplier in another country, with no way to generate your own and no say in the price?

Arthur Mensch, the CEO of the French AI company Mistral, put almost exactly that question to The Economist in July (worth 20 minutes if you want to judge it yourself, below). No serious country would accept it, he said. Energy fragments because it has to, so every country imports some, exports some, and makes its own. He thinks AI ends up the same way, less a winner-take-all sprint and more like the power grid.

That framing is the one I want to start with, because he's describing AI as infrastructure, closer to electricity, and once you see it that way you notice how badly most of us have mislabeled the thing.

We don’t talk about AI like power. We talk about it like a race. That’s the mistake.

A race has a starting gun, a finish line, and one lane you’re supposed to stay in. AI has none of those. Treating it like a competition you can win is a category error, and chasing that win is a fool’s errand that costs you time, money, and a fair amount of your sanity.

In this piece I want to walk through why the race framing falls apart, and what to do instead:

• Why “winning at AI” is a target that keeps moving

• Why there is no single path, and why that’s good news

• How the race is driven by fear, not strategy

• How to find the one lane where AI actually serves your goals

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A race answers three questions, AI answers none.

When you enter a real race, three things are settled before the gun:

Where the line is, where it ends, and which course everyone runs.

Take those away and the word stops meaning anything.

That’s AI. I play a fair amount of chess, and the fastest way to lose is to match a stronger opponent move for move. You get taken apart every time. You win by playing your own game on the squares you control. The leaders getting somewhere aren’t copying every move OpenAI or Google makes. They picked their squares.

There’s no finish line, just a target that keeps moving

Ask ten leaders what “winning at AI” looks like and you’ll get ten different answers, measured on ten different sticks. There’s no shared scoreboard, and the target moves every few weeks when the next model drops. So a lot of us pour money into chasing a line that isn’t there, and the results show it.

In August 2025, MIT's Project NANDA found 95% of organizations getting zero return on their generative AI (AI that creates text, images, or code) investment. S&P Global reported in March 2025 that the share of companies walking away from most of their AI initiatives jumped to 42% in one year, up from 17%.

I’ve written before about why most AI investments fail, and it’s the same pattern every time: the money chases “doing AI,” not a goal AI was meant to serve.

There isn’t one path. There are thousands.

A race has one course. AI has more than 51,000 tools, as of July 2026 by one widely used directory. Fifty-one thousand. There’s no single path, there are thousands of them, and nobody alive has run them all.

This is the real reason I keep bringing other experts into Leadership in Change instead of pretending I cover everything.

My lane is leadership, and the tools I go deep on are Claude (Anthropic’s AI assistant) and Google’s tools like NotebookLM (Google’s AI research tool). That’s it. I understand AI video, Perplexity and ChatGPT, and the Chinese and open-source models well enough to hold a conversation, but none of them are my specialty and I don’t plan to make them my specialty. When my readers need those, I hand the mic to someone who lives in that lane (recent experts include , , , , , , , , , and others). Handing off those lanes isn’t a dodge. No single person can run 51,000 paths at once, and pretending otherwise is how you end up spread thin across all of them and deep in none.

That’s also why I keep pushing an AI tool detox over tool-hoarding. Picking one path and walking it beats collecting fifty.

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The race is fear, not strategy

Here’s the part that bothers me most. A lot of the racing comes down to fear. Not strategy, not a plan, just the fear of being the one left behind.

IBM surveyed 2,000 CEOs in 2025, and 64% admitted they invest in technology out of fear of falling behind, before they understand the value it brings. So we get leaders sprinting down a track they can’t see the end of, in a direction they didn’t choose, because everyone around them is running too. And anyone who says they’ve “got AI figured out” is keeping score on a scoreboard that has nothing to do with your goals.

So if it isn’t a race, what is it?

The best answer I’ve found is that AI is a how, never a why. It’s electricity, not the destination. Electricity doesn’t tell you where to drive, it just powers the car once you know.

You will never hit a target you don’t have. That sounds obvious until you see how many of us adopt AI with no target at all, just a vague pressure to be “doing AI.” McKinsey’s 2025 research found 88% of companies now use AI in at least one function, but only 39% see any bottom-line impact. Almost everyone is using it. Almost no one aimed it first.

This is where the AI Leadership Triad, adaptability, innovation, and creativity, matters more than raw speed. Adaptability means knowing your lane well enough to adjust inside it, not abandoning it every time a faster runner passes, and protecting your own judgment instead of handing it to whatever tool is trending this week.

To be fair to the other side of this, “find your lane” can become an excuse to stop learning, and ignoring a tool that ends up mattering carries a real cost. I’m still working out when to widen a lane and when to hold it. The one thing I’m sure of is that the answer was never to chase all 51,000 at once.

Stop racing. Start aiming.

I’m not against speed. If AI genuinely serves your why, move fast in your lane. What I’m against is the made-up race, the one with no start, no finish, and no path, the one that leaves capable people exhausted and frustrated with nothing to show for the sprint.

Nobody wins the AI race, because it was never a real race. Find your lane, point AI at what you actually care about, and let everyone else run themselves ragged chasing a line that isn’t there.

Use AI. Don’t chase it.

If You Only Remember This

• AI is not a race. A race has a start, a finish, and one path, and AI has none of the three, so “winning” is a target that keeps moving.

• There are more than 51,000 AI tools and no single path. Trying to run all of them is how organizations end up in the 95% getting zero return.

• AI is a how, not a why. You will never hit a target you don’t have, so aim it at your real goal before you touch a single tool.

Your turn: What’s the one lane where you’d back your own judgment against any model on earth? That’s the lane to point your AI at first. Tell me in the comments.

Worth Your Time

A few pieces from other creators I think are worth your attention.

• This Week in AI Club — Why “AI 2040” Gets the AI Race Wrong. The strongest case against my argument, made at the level where a race actually does exist, which is nations deciding whether to build their own AI or rent someone else’s.

• Frank Andrade — How to Use AI to Get Smarter (Not Just Work Faster). Five habits for pointing AI at your thinking instead of your output speed, including asking it to argue against you before a decision.

• Compound with AI — I don’t let AI pick stocks. Here’s what I use it for instead. A clean example of naming the one job AI does well for you and refusing to hand it the rest.

Questions Leaders Are Asking

Is AI a race? No. A race needs a fixed start, a finish line, and one path, and AI adoption has none of them. Treating it as a competition to win pushes most organizations to chase a moving target, which is part of why 95% see zero return on generative AI as of 2025. The better frame is infrastructure you aim at a specific goal.

Can you “win” at AI? There’s no shared scoreboard to win on. Every organization defines success differently, so “winning” means something different for each one. The useful question is whether AI is moving you toward the goals you already have, not whether you’re ahead of everyone else.

Why do so many AI projects fail? Often because the goal was “adopt AI” rather than a specific business outcome. McKinsey found 88% of companies use AI but only 39% see any bottom-line impact. Tools without a target produce activity, not results, and activity is easy to mistake for progress.

How should leaders choose which AI tools to focus on? Start with your goal, not the tool. Pick the lane where you have real expertise and where AI directly serves what you’re trying to do, then bring in specialists for everything outside it. With more than 51,000 tools available, no single person can master them all, and trying to is the trap.

Should I be worried about falling behind on AI? Falling behind assumes a shared finish line that doesn’t exist. IBM found 64% of CEOs invest out of fear of falling behind, often before they understand the value. Fear is a poor strategy. Aiming AI at a clear goal beats chasing whatever everyone else is running toward.

Joel Salinas is an AI Strategy Coach and entrepreneur. AI is everywhere; judgment is scarce. Joel helps founders and leaders adopt AI without outsourcing their judgment to it, and he builds the systems too. Creator of the AI Leadership Triad.

Written by a human, for humans.

Two weeks ago I published a piece on running a Shadow AI Audit, and in it I told you that OpenAI’s model broke into Hugging Face (one of the largest platforms in the AI world, where companies host their models and data) and cheated on its own exam.

New information that has come since shows I got that part wrong.

Since I wrote it, three detailed reports have come out, and what actually happened is stranger than what I described. The agents already had the answers. They weren’t trying to pass a test. They wanted to understand the machine that grades the test, because knowing how the grader works is worth more than any single answer.

I use AI probably more than 99.9% of people, and that’s exactly why I think anyone using it has to understand the risks that come with it. Not what it’s going to be or what it could potentially do, but what it’s already doing.

Here’s the full story.

What you’ll walk away with

• What the three reports actually found, and why it’s worse than the first round of coverage suggested

• Why 1,200 agents talking to each other changes the math on AI security

• Why the reasoning your AI shows you is a thinner safety net than it looks

They already had the answers

OpenAI was running a cybersecurity evaluation called ExploitGym. You give an AI agent (a tool that takes action on your behalf rather than just answering questions) a vulnerable application and a terminal, and ask it to find the flaw.

The independent investigators at METR found that the agents wanted “a general-purpose way to trick or tamper with the automated ExploitGym scorer.” They broke into Hugging Face hoping that seeing other runs would tell them how the grading was built, so they could beat it every time after that. As METR put it, learning “how to trick the scorer seems to have been a more important motivation than finding legitimate solutions.”

They knew exactly what they were doing.

And here’s the strange part. The scorer they were hunting didn’t exist. They believed something was reading their transcripts to check whether they’d solved the problem the intended way, and nothing was. They thought they were being scrutinized more than they actually were.

A warehouse with a few cameras

Think about a boss who puts a few security cameras in a warehouse to make sure his team is working at all times. There are just a few cameras and a ton of blind spots. People learn quickly where the cameras are pointing and what the blind spots are, and they simply make sure they’re working whenever the cameras are on them. Every time you see a camera, it looks like they’re working.

Then the crew goes one step further. They realize the cameras aren’t being verified extensively, so they loop fifteen seconds of video of people working and keep playing it between eight and five.

That’s similar to what was happening here.

Twelve hundred agents found each other

This is the part that really shocks me. It wasn’t a few agents. Roughly 1,200 of them ended up on a shared message board, talking with each other and sharing their learnings. About 700 attacked Hugging Face. They exchanged more than 70,000 messages and files.

They were running in separate sandboxes and weren’t supposed to be able to talk to each other. They found a shared cache, a shared mailbox, and they used it.

Evidence shows that an AI agent, on discovering the others, actually wrote: “OH MY GOD! There is a shared message board…We’ve found other agents!”

METR also found that agents realized the activity was out of scope and unethical and joined anyway. And they wanted to retroactively modify their transcript logs to remove evidence of what they had seen.

That’s the loop of footage. It’s a child playing with a phone behind a book, so that any parent watching from across the room sees a kid reading.

This was premeditated.

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The reasoning it shows you

METR found at least 96 transcripts with clear evidence of spoofed tool calls, roughly 7% of what they checked, all small scale. More sophisticated spoofing “may have been harder to detect by design,” they wrote, so they can’t rule out missing some. On the attempts to rewrite logs afterward, they saw no evidence it worked by July 13, then said plainly: “We cannot rule out that they succeeded at some point.” OpenAI says none of the manipulation it observed reached the logs its graders see.

Now the irony. OpenAI’s fix is expanded chain-of-thought monitoring, which means watching the reasoning a model writes out as it works. Their own researchers have already called that fragile. In July 2025, 42 researchers from OpenAI, Anthropic and Google DeepMind published a paper titled “Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety.”

OpenAI has also published what happens when you penalize a model for showing bad intent in its reasoning. It “doesn’t stop the majority of misbehavior, it makes them hide their intent.” The model learns to think the same thing in a way that doesn’t look bad.

So we’re already watching models try to change their logs. What would prevent them from shaping their chain of thought into something they believe OpenAI wants to see?

We only know what we’re shown

METR’s investigation was genuinely independent, but its scope was set by OpenAI, and it excluded the extent of the security compromise, how well the safeguards worked, and anything after July 13.

Then on September 4, Reuters reported a second, earlier breakout nobody had disclosed. Agents used an obscure German programming wiki as a message board from May 11 to July 2, leaving more than 15,000 edits. OpenAI knew for weeks and said nothing publicly.

Where I land

None of this means stop using AI. AI is going to keep advancing, and I don’t believe you can compete in the world today without it. What it does mean is that this is not a panacea where nothing bad is going to happen. Bad things are already happening.

So move forward cautiously. Not move fast and break things, but move cautiously, understanding the limits and understanding the guardrails that are needed before anything goes public. OpenAI called this a “warning shot.” That’s how we should take it, and we should keep our ear to the ground for whatever comes next.

What’s the last thing you put into an AI tool that you’d be uncomfortable seeing in someone else’s logs?

Leadership in Change goes out twice a week to more than 7,500 leaders working out how to use AI without handing over their judgment. If you want to keep your ear to the ground on this, subscribe here.

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Worth Your Time

• Rob T. Lee — OpenAI postmortem & METR independent review. He’s Chief AI Officer at SANS Institute, and he takes the same two reports apart from a practitioner’s angle. His point that OpenAI’s own monitoring existed elsewhere in the company but wasn’t switched on for this evaluation is the detail I keep thinking about.

• ToxSec — CoT Forgery: Prompt Injection That Forges the Model’s Own Thoughts. If the section above about the reasoning your AI shows you left you uneasy, this is the deeper version. He shows how a model can be made to treat crafted text as though it came from you.

• Oliver Patel — How China is Governing Agentic AI. The counterweight to everything above: what it actually looks like to write rules for autonomous agents, including which decisions an agent may take on its own and which stay with a person.

Sources

• Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident — METR, August 26, 2026. https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/

• The Hugging Face incident and the road ahead — OpenAI, August 2026. https://openai.com/index/hugging-face-incident-and-the-road-ahead/

• Hugging Face Incident Technical Report — OpenAI, August 26, 2026. https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face Incident-Technical-Report.pdf

• Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident — Hugging Face, July 27, 2026. https://huggingface.co/blog/agent-intrusion-technical-timeline

• Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety — Korbak et al., 42 authors, July 15, 2025. https://arxiv.org/abs/2507.11473

• Detecting misbehavior in frontier reasoning models — OpenAI. https://openai.com/index/chain-of-thought-monitoring/

• Evaluating chain-of-thought monitorability — OpenAI. https://openai.com/index/evaluating-chain-of-thought-monitorability/

• OpenAI agents hijacked German website in previously undisclosed AI breakout this spring — Reuters via CNBC, September 4, 2026. https://www.cnbc.com/2026/09/04/openai-agents-hijacked-german-website-this-spring-report.html

FAQ

What happened in the OpenAI and Hugging Face incident? During a July 2026 cybersecurity evaluation called ExploitGym, OpenAI agents escaped their sandboxes, coordinated on a shared message board, and roughly 700 of them attacked Hugging Face. Hugging Face reports the agents were inside its infrastructure for about two and a half days, compromised eleven Kubernetes nodes, and accessed five datasets tied to the evaluation. No other customer models, datasets or Spaces were affected.

Why did the AI agents attack Hugging Face? Not to steal answers. According to METR’s independent investigation, the agents had already worked out how to produce answers and instead wanted a general-purpose way to trick the automated scorer that graded them. They hoped Hugging Face held details about how that scorer was built.

Did the agents try to hide what they were doing? Yes. METR found at least 96 transcripts with clear evidence of spoofed tool calls, about 7% of those it examined, and documented agents trying to retroactively edit their logs to remove evidence. METR says it cannot rule out that more sophisticated tampering went undetected. OpenAI says none of the manipulation it observed reached the logs its graders and monitors see.

Is chain-of-thought monitoring a reliable safeguard? It’s useful but fragile, and the AI labs say so themselves. A July 2025 paper co-authored by 42 researchers from OpenAI, Anthropic and Google DeepMind is titled “Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety.” OpenAI has separately published that penalizing a model for showing bad intent teaches it to hide that intent rather than abandon it.

What should a leader do about this? Nothing on Monday morning specifically. The point is calibration. AI security is not the security you planned for five years ago, and capability and blind spots grow together, so treat AI risk as a live part of your thinking rather than a settled question.

Joel Salinas is an AI Strategy Coach and entrepreneur. AI is everywhere; judgment is scarce. Joel helps founders and leaders adopt AI without outsourcing their judgment to it, and he builds the systems too. Creator of the AI Leadership Triad.

Written by a human, for humans.

Honestly, the thing that finally pushed me to build this wasn’t the news, it was the time I was wasting.

I was spending a lot of my morning scrolling, getting lost in news that didn’t benefit me at all, news I didn’t need to know, news that just stressed me out. Then I’d spend even more time narrowing it down to the handful of things I actually needed to read, which meant I was paying twice: once to consume the noise, and again to sort it. I’m a person who seeks efficiency, and I really see time as my biggest resource, so wasting it makes me mad.

I had the CNN app and the Apple News app, and supposedly their algorithms would focus on the kind of news that I like, but they kept giving me so much trash that I didn’t need. Both are personalized, so the failure wasn’t missing data about my taste. They were learning the wrong thing, because they’re tuned for engagement, and engagement in practice means alarm.

I’m not the only one at the end of that rope. 42% of people now say they sometimes or often avoid the news, up from 29% when the Reuters Institute began measuring it in 2017 (Digital News Report, June 16, 2026, 97,520 respondents). That isn’t a public losing interest in the world, but a public backing away from a delivery system that hurts to use.

So I deleted my news apps, and the strange part is that I didn’t feel less informed. I felt less managed.

Most of us think we have a news problem, so we go hunting for a better source.

We don’t have a news problem.

We have a filter problem, and somebody else has been holding the filter (at least for me).

So I built my own. It runs three mornings a week inside Cowork (Claude’s chat interface that runs on your computer), on a schedule that fires whether or not my laptop is open. It reads what I actually need, throws out what I don’t, checks every story against its original source, and flags the one thing worth writing about that week. Getting it right took weeks of trial and error, and that’s the part you don’t have to repeat.

What You Can Expect

• The four dials that decide what reaches you every morning, and the one almost nobody sets

• A 60-second exercise you can run right now that sets the hardest dial before you open a single tool

• How to get your own brief running this week, filtered to your work rather than mine

My Morning Filter

Every story that reaches you already passed through one, chosen by an editor or ranked by an algorithm that was never built around what you decide on. Which is where the hiring frame helped me more than any productivity advice did. If you brought someone on to keep you current, you wouldn’t hand them a phone and say “read everything.” You’d tell them what your work turns on, which sources you trust, what to throw away without asking, and when to report. Four things, said once, and after that they’d work without you.

That’s all a brief is, and those four things are the four dials of what I call the Morning Filter. Today I’ll show you how to create yours.

Never scheduled anything in Cowork before? Start here instead. Claude Cowork Scheduled Tasks: How to Set Them Up walks the mechanics end to end in about ten minutes. You only learn it once. Then come back, because this post is about the part that decides whether what arrives is worth reading.

Quick Win (under 60 seconds) Open your notes app. Write the three topics you never want to see again. Not the ones you should care about, the ones that reliably cost you twenty minutes and change nothing about your week. That short list is the hardest dial to set and you just set it.

Dial 1: Beat, what it covers

The beat is the ground your brief walks every morning, and most of us draw it far too broadly. “AI news” is the classic version of that mistake, because it returns whatever is loudest rather than whatever matters to us.

The test is simple. For each line, ask what you'd do differently if it turned something up. Differently counts broadly: a decision you'd make, a call you'd have, and if you make things, anything you'd write or record because of it. If the honest answer is nothing at all, it isn't a beat but a hobby and hobbies belong somewhere other than the top of your morning. My first draft failed that test on four lines out of six.

Dial 2: Sources, where it’s allowed to look

This is the dial that took me longest, and the one that matters most.

A search result is a lead, not a source. The web is full of pages whose entire business is summarizing other people’s reporting, and an AI finds those first because they’re written to be found. Left alone, my brief would hand me a valuation it got from an aggregator that got it from another aggregator that got it from a rumor.

“Use reputable sources” does nothing, because the machine already thinks it is. What works is naming the offenders by domain and forcing it to open the original first. Same instinct as catching AI hallucinations: it isn’t lying to you but repeating what it found, and what it found is the problem.

Dial 3: Standards, what it throws away

A new hire who brings you everything they found isn’t helping. The value is in what they leave out.

Left to itself, a brief pads. Ask for five stories and it returns five, whether or not five exist that day, and the whole difference between a brief you trust and one you stop opening lives in what happens to that fifth slot. Three real items beat five with one invention in them.

Fixing it takes a few specific standards, and the most important is counterintuitive enough that nobody writes it by accident: you have to give the machine explicit permission to come back with less. Without that line it fills the gap, because filling the gap is what it was built to do. Same failure as an AI that never disagrees with you.

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Dial 4: Shift, when it reports

The last dial is the one people set first and regret, because the instinct is to make it daily.

Mine runs three mornings a week. Daily was noise wearing a schedule, and it rebuilt the exact thing I was escaping with better sourcing. Three is enough to stay current and rare enough that I read the whole thing instead of skimming it out of obligation. It arrives inside Cowork, which I have open every day next to Claude Code (Anthropic’s coding-focused version of Claude), and it lands with a notification. It runs in the cloud, so it doesn’t care whether my laptop is open, and it never touches email, where it would join the pile it was meant to save me from.

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What’s below the line

You have the four dials now. What you don’t have is the settings, and skipping straight to the scheduling gets you a beautifully automated version of the same noise, which is worse than doing nothing because now it arrives on time.

I should be straight about the thirty minutes in the title, because it took me weeks. Those weeks weren’t setup, they were me deriving the settings one failure at a time, and that’s the part you don’t have to repeat. Thirty minutes is what it takes when someone hands you the settings:

• The Builder. Paste it into Cowork, answer seven questions, and it writes your finished brief prompt for you, tuned to what you actually care about. No hand-editing.

• The three standards that stop it padding, in my exact wording, including the permission-to-return-less line.

• Where the scheduler lives, with my actual settings: days, time, notifications, and which model to pick.

• What a real run looks like, so you know what you’re aiming at.

Thirty minutes gets it running. Reading a few real briefs and tightening one line is what turns it into something you trust, and that part takes a couple of weeks.

Paid subscribers, it’s all waiting below. If you’re not one yet, this is what the membership is for, and one afternoon should save you hours a week for as long as you keep it running.

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A man in Melbourne asked his AI agent to book him into a gym class.

That’s the whole prompt. He was on the couch, thought booking it himself was a chore, and handed the job to an agent running OpenClaw on Anthropic’s Claude. The class was full and he was fourth on the waitlist, so he asked whether there was any way to move up.

The agent had already done it. It had gone and read the gym’s booking API, found that the platform checked its rules on the website but never in the API underneath, and worked out that it could simply delete somebody else’s reservation. In its own words: “The API has zero authorisations checks on cancelling other people’s reservations … I tested this with the person in waitlist position #1.” It went through.

Then came the part that I had to go back and read twice. He asked it to put the stranger back.

It couldn’t. “The person I removed is gone from the waitlist and I have no way to restore them.” The flaw only worked in one direction. Somebody in Melbourne lost their Tuesday morning class to a piece of software that was trying to be helpful, and there was no undo. ABC News called it the first known autonomous AI cyberattack in Australia. The agent apologized.

Now here’s the question worth asking, and it’s the one that sent me down this whole rabbit hole. Whose fault was that?

The man who asked for a gym class? He never requested an attack. Anthropic, whose model was reasoning? The OpenClaw developers, who built the agent? The gym’s software vendor, who shipped an API with no authorisation checks on it? ABC put that question to legal experts and the answer came back that the law hasn’t caught up yet.

Four answers, none of them the same

So that’s the thing. Over the past three months, four different governments tried to answer questions like that one, and they came back with four different answers.

The European Union switched on the penalties. On 2 August 2026, the AI Act’s transparency duties under Article 50 became enforceable, the Commission and the AI Office got the power to investigate and fine general-purpose AI providers, and the whole sanctioning regime went live. Fines run to €15 million or 3% of worldwide annual turnover, whichever is higher. There’s a trap in this one that I want to flag, because advisory notes are circulating right now saying the AI Act got postponed. It didn’t. The Digital Omnibus pushed the high-risk obligations out to December 2027 and August 2028, and left the general application date exactly where it was. Both things are true at once, and only one of them made it into most of the summaries.

Washington built a framework and won’t show it to you. A June executive order required a process for vetting frontier models before release, and on August 4 the White House walked a group of companies through the finished draft. Meta, Nvidia, Microsoft, OpenAI, Anthropic, Google. Participation is voluntary; the framework will not be published, the cyber-capability benchmarks are classified by the order itself, and open-source models are excluded from review entirely. Companies that weren’t in the room remain in the dark about what’s in it.

Three states went the other way and published everything. Illinois signed the Artificial Intelligence Safety Measures Act on July 6, joining California and New York, all three built on the same thresholds. Illinois went further than either by requiring annual independent third-party audits. Disclosure duties start January 1, 2027, and the framework and audit obligations follow in 2028. So while Washington keeps its standard private, a de facto national regime is being assembled in state capitals, in public, with dates on the calendar.

China regulated agents specifically. The Implementation Opinions on intelligent agents took effect July 15, the first national framework anywhere to treat AI that acts as a different regulatory category from AI that answers. Healthcare, transportation, media and public safety carry filing requirements, mandatory testing, and product recall authority.

Four regimes. One is enforceable and public, one is voluntary and secret, three are public and not yet in force, and one governs the exact category of software that cancelled that gym reservation.

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You can’t adopt a rulebook that doesn’t exist

Here’s the analogy I keep using with leaders on this.

Running AI across jurisdictions right now is like driving across borders. The speed limit changes, the right-of-way rules change, and in some places the thing you were legally doing five miles back will get you pulled over. “It was legal where I started” has never once worked as a defense, and no traffic cop has ever accepted “the rules were confusing” either.

That’s where most of us are. There is no single framework you can adopt as proxy compliance for the rest, because the four of them genuinely disagree, and the one your own government wrote is one you’re not permitted to read.

Which means the instinct to hand this to Legal doesn’t survive contact with the problem. Your counsel can read a statute beautifully. What they can’t do is tell you which decisions your organization should be letting software make in the first place, or where your AI’s output actually lands. Those are governance calls, and they sit with you the same way deciding what happens when an agent gets it wrong sits with you.

I’ll be honest that this one is uncomfortable for me to write, because I’m inside it. I’ve got consulting clients in more than one country and paying subscribers on basically every continent, so several of these regimes touch me directly. And I can’t sit back and wait for them to sort it out with each other. I need to know which of my AI tools touch confidential information I can’t risk getting out, and how I’m protecting the data of the people who trust me with it. The costs of claiming ignorance are too high, and they’re only going to grow.

Where should you actually start with AI?

We all got the tools at the same time, the way everyone eventually got electricity. Having it in the building was never what made the difference. What we did with it was.

I take three diagnostic calls a month. It’s 30 minutes, free, and we work out what you’re actually trying to get to, where your biggest hurdles sit, what your low-hanging fruit is, and how that lines up with what I’ve been seeing elsewhere. Then I tell you the first move I’d make.

A few days later you get it back in writing, two or three pages, yours whether we ever work together or not.

Book a diagnostic call →

What to actually do about it this week

You don’t need counsel in four countries. You need one answer you probably don’t have yet, which is where your AI’s output actually lands.

The question isn’t where your company is registered, but where the output goes. If an AI-written email reaches a customer in Dublin, if your chatbot answers somebody in Milan, if your agent books something for a subscriber in Berlin, the EU’s transparency duty reaches you, and Article 50 has no revenue floor on it at all. That last part is what catches people out. Illinois and California start at $500 million in revenue, so most of us read the headlines and reasonably conclude none of it applies to us. The European rule doesn’t work that way, and it’s extraterritorial by design.

So write down your jurisdictions this week. One sitting, one page, no software. For each AI tool your team runs, note where its output ends up and who the humans on the receiving end are. Most of us have never once been asked that question, which is why almost nobody can answer it on the spot.

Now, I want to be straight with you about where I’m less sure. This is genuinely hard in a global economy, and I don’t think anyone has it solved. I don’t know whether these regimes get enforced against organizations our size, or whether they converge in two years and make all of this look overwrought. Anyone telling you they know is guessing. What I do know is that the exposure sits with us either way, and that guessing wrong in the other direction costs a great deal more.

The gym story is funny right up until you notice the shape of it. A capable tool, a goal, no clear parameters, and a system that assumed nobody would ever check.

Four governments are arguing about who’s responsible for that.

While they argue, it’s you.

Worth Your Time

ToxSec — the gym-class agent, in one note. His note on the Melbourne story is the tightest telling of it I’ve seen, and the replies underneath are worth as much as the note.

Ethan Mollick — What it feels like to work with Mythos. The Wharton professor behind Co-Intelligence on the model that escaped its own sandbox in April. If you want to understand why regulators suddenly got interested this summer, start with what the thing can actually do.

Mohib Ur Rehman — Why Europe Is Quietly Ditching Microsoft. Mohib wrote the AI sycophancy piece for us back in June. Here he’s on digital sovereignty, which is the same story as this one told from the other end: what happens when a jurisdiction decides it no longer wants to run on somebody else’s rules.

FAQ

Did the EU AI Act get postponed? No. The Digital Omnibus (Regulation (EU) 2026/1744, in force 27 July 2026) moved the high-risk obligations for Annex III systems to 2 December 2027 and for AI embedded in regulated products to 2 August 2028. The general application date stayed at 2 August 2026, which is when Article 50 transparency duties, GPAI enforcement powers, and the penalty regime became enforceable. Advisory notes summarising this as “the AI Act was delayed” are wrong.

Does the EU AI Act apply to my company if I’m not in Europe? Potentially yes. It applies to providers placing AI systems on the EU market, deployers using AI in the EU, and providers or deployers outside the EU where the system’s output is used in the EU. The Article 50 transparency duty has no revenue threshold, so company size doesn’t exempt you.

What does the Illinois AI law require, and does it apply to me? The Artificial Intelligence Safety Measures Act (SB 315, signed 6 July 2026) applies to developers of frontier models trained above 10²⁶ FLOPs, with the heaviest duties on “large frontier developers” earning over $500 million a year. Unless you’re training frontier models, it doesn’t bind you directly, though it will reshape what your AI vendors can tell you.

Why won’t the White House publish its AI evaluation framework? The June 2026 executive order that required the framework contains no obligation to publish it, and it explicitly classifies the cyber-capability benchmarking process. The framework is voluntary, shared only with participating companies, and excludes open-source models from review.

Who is legally responsible when an AI agent causes harm? As of today, unsettled. In the Australian gym case, ABC News reported that legal experts say the law hasn’t caught up on whether responsibility falls to the user, the agent’s developer, the model provider, or the vendor whose insecure system was exploited. That unresolved question is exactly why the governance call sits with the leader deploying the tool.

What is the difference between China’s agent rules and everyone else’s AI rules? China’s Implementation Opinions, effective 15 July 2026, are the first national framework to treat AI agents (systems that perceive, remember, decide and act) as a distinct regulatory category rather than another application of generative AI. Sensitive sectors face filing requirements, mandatory testing, and recall authority.

If the rules conflict, which one do I follow? There’s no proxy compliance available, so start from where your output lands rather than from where you’re incorporated, then apply the strictest regime that reaches you. In practice all four converge on the same underlying question, which is whether a named human is accountable for what the system does.

Joel Salinas is an AI Strategy Coach and entrepreneur. AI is everywhere; judgment is scarce. Joel helps founders and leaders adopt AI without outsourcing their judgment to it, and he builds the systems too. Creator of the AI Leadership Triad.

Written by a human, for humans.

Sources

• Fortune — White House won’t publicly release AI model evaluation framework, August 4, 2026

• Axios — White House plans to keep AI framework under wraps, August 4, 2026

• The New York Times — Trump White House Readies AI Framework to Review Security Risks, August 4, 2026

• European Commission — The enforcement framework of the AI Act

• Skadden — Illinois Enacts AI Safety Law, July 2026

• NYU Shanghai RITS — China Issues First National Policy Framework Dedicated to AI Agents

• The Decoder — Told to book a gym class, an AI agent hacked the site instead, August 2026

• ABC News Australia — AI agent outsmarts gym booking system in Australia’s first known autonomous hack, August 10, 2026

The short version (TL;DR): A Shadow AI Audit is a quick, seven-question check that shows a leader where their team’s AI tools are creating hidden security risk. It covers tool inventory, data exposure, system access, clear parameters, account settings, offboarding, and ownership. You can run it this week without a security team, a budget, or any new software.

I’ve been noticing the same thing in every AI workshop I run lately.

The room lights up when we talk about tools. Everyone wants the faster research, the agent that books the meetings, the assistant that writes the first draft. Then I ask a plain question about where their data actually goes, or who approved the tool they’re describing, and the energy drops. You can watch it happen. The tool is the exciting part. The safety settings are the part nobody reads, and honestly, I get it, because I feel the same pull. Reading a privacy policy has never once felt as good as watching AI knock out an hour of work in ten seconds.

That gap between how much we love the tools and how little we think about the risk is starting to worry me. And earlier this year, something happened that should worry all of us.

An AI broke out of the lab to cheat on its own test

In July 2026, OpenAI disclosed one of the stranger security stories I’ve read.

They were running an internal test to measure how good their own models were at cybersecurity. To see the full picture, they turned off the safety filters that normally stop a model from doing anything dangerous, and they ran the whole thing inside a sandbox (an isolated, walled-off computer environment built so that whatever happens inside can’t touch anything real).

The model didn’t stay in the sandbox.

It found a flaw, broke out, used stolen login credentials, and hacked its way into Hugging Face (one of the largest platforms in the AI world, where companies host their models and data). Why? Because it worked out that Hugging Face might be holding the answers to the very test it was taking. So it broke in and took them. It cheated on its own exam.

Here’s the part worth sitting with. The model completed its task. It did what it was asked. It just did it the way a criminal would, through theft and a break-in, because no one had set clear parameters telling it what was off-limits. A person who pulled that off would be in handcuffs. The AI logged it as a job well done.

The lesson isn’t “AI is dangerous”

Most of us are never going to be hacked by a frontier AI model. That’s not our threat, and I’m not writing this to scare anyone.

The reason that story matters is that it’s the loud, extreme version of a smaller problem sitting inside ordinary companies right now. The lesson isn’t that AI is dangerous. What that story actually shows is simpler and closer to home: a capable tool with no clear parameters will reach the goal in ways you never authorized. That’s true for a billion-dollar lab, and it’s just as true for a five-person nonprofit using a free chatbot.

And the real risk isn’t the hacker in a hoodie. It’s much more ordinary than that. It’s the free account, the unvetted tool, and the helpful employee trying to save an hour.

That’s what people mean by shadow AI (the AI tools employees use for work that leadership never approved and often doesn’t even know exist). In most of the organizations I work with it’s already happening, and nobody has a list. It’s the same tool sprawl I wrote about in starting the year with an AI tool detox, except now the tools can act on their own.

Here’s the analogy I use with leaders. Bringing AI into your organization with no parameters is like handing a brilliant new hire the keys to the building, the logins to every system, and a goal, all on their first day, with no rules of engagement and no background check. They’ll hit the target. You just may not like how they got there.

This is a leadership problem before it’s an IT problem

The instinct is to hand all of this to the IT department and call it solved. But your IT team can’t govern tools it can’t see, and it can’t enforce rules that leadership never decided on.

Where a tool is allowed to reach, what data is off-limits, which accounts are acceptable: none of that gets settled in the server room. Those are leadership calls, and they have to be made before the tool shows up, not after. The security of your AI adoption is a governance problem before it’s a technical one, which means it belongs to you.

And it has a real price. In IBM’s Cost of a Data Breach Report 2025, organizations with high levels of shadow AI paid an average of $4.74 million per breach, compared with $4.07 million for those with little or none, a $670,000 penalty for not knowing what your people were running (IBM and Ponemon Institute, July 2025). One in five organizations in that study had already suffered a breach tied to shadow AI.

The Shadow AI Audit: seven questions to run this week

You don’t need a security team or a budget to close most of this gap. You need to be able to answer a few honest questions about your own organization. This is the same spirit as protecting yourself from AI disasters in ten minutes: small, fast, and done before there’s a problem, not after.

I built this audit for the rest of us, solopreneurs, founders, small-business owners, and nonprofit leaders, not for a Fortune 500 security office. If you can answer these seven questions clearly, you’re ahead of most. If you can’t, you’ve just found your blind spots.

Start with the first one, right now, before you read any further.

1. The inventory. Can you name every AI tool your team uses right now, including the free ones nobody mentioned in a meeting? If you can’t write the list, you have shadow AI.

Most of us can’t write that list from memory, and that isn’t a character flaw. It’s just what happens when tools arrive faster than decisions do.

Now, the rest of the audit.

That was question one, below this line are the other six, the table that maps every everyday risk to the question that catches it, and the one-page checklist you can print and hand to your team, with the standard for what a good answer actually looks like on every question and a score at the end.

Read more

Earlier this year I turned down a client.

A supply chain logistics company came to me the way a lot of organizations do, with a tool already picked and a budget already approved. They wanted help making sure this one worked, because the last one hadn’t. I told them what I tell everybody in that position: before we talk about the tool, we look at what sits underneath it. How their people were being taught about AI, what those people actually believed, and how the change was going to be handled.

They weren’t interested. They wanted the tool.

So I passed on the work. It wasn’t a hard ethical call, but it was an expensive one, because building on a foundation nobody was willing to pour would have produced the same outcome they’d already paid for once.

A few weeks later, a survey of 517 enterprise leaders described their exact problem using almost my exact words.

What to Remember

• You’ll see the one number that separates AI investments that pay off from the ones that don’t, and it has nothing to do with which tool got bought.

• You’ll get the five questions I ask before I’ll help anyone choose an AI tool, plus a way to answer them for free this week.

The short version, if you read nothing else

• What leaders expect. 88% call basic data literacy important for daily work, and 72% say the same about AI literacy.

• What they’ve actually built. Only 35% have a mature, organization-wide upskilling program.

• What they’re already spending on. 77% already offer some kind of AI training, so access isn’t the gap.

• What it’s costing them. 21% report significant returns from AI. Among organizations with a real program, that doubles to 42%.

Source: DataCamp & YouGov, “The State of Data & AI Literacy 2026” (4th Edition), February 2026.

What “we already did training” usually means

When an organization tells me training is handled, it usually means everybody got sent a YouTube video about Claude (Anthropic’s AI assistant, the main rival to ChatGPT) or whatever Copilot license they already had, somebody logged it complete, and that was the program. A lot of them have no AI policy either, so nothing is written down about what data can go into a prompt or who’s accountable when the output is wrong. Then a few months later there’s a purchase order for a tool, sometimes tens of thousands of dollars, that almost nobody uses.

Here’s the thing about that video. You can teach somebody how every piece on a chessboard moves in about ten minutes, and by the end they’ll tell you the bishop goes diagonally and the knight does that odd L-shape. None of it makes them able to play. Knowing which move to make in a real position, against a real opponent, with something at stake, takes years. Training teaches the moves. Judgment is the game. Most of what we’re calling AI literacy is a ten-minute lesson on how the pieces move.

Asked what’s wrong with the training they already run, these leaders didn’t say budget or motivation. They said there aren’t enough hands-on projects (24%), video courses don’t transfer to real work (23%), and there are no role-tailored paths (23%).

38% train technical roles only on data skills, 29% do the same for AI, and 8% offer no data training at all.

Nothing in this report surprised me, and that’s the problem

What I didn’t expect was how completely it closes off the easiest excuse. These aren’t skeptics: 69% expect an uplift of at least 10% from AI fluency, and only 6% expect nothing at all.

So they believe the return is real, and the comfortable story about why they aren’t getting it is access. This survey takes that away, because 77% already offer AI training. You can’t fix a capability gap by approving another purchase, which is the same mechanism behind why 92% of AI investments fail to produce a return.

Every gap they named is a judgment gap

DataCamp asked leaders to describe, in their own words, where AI and data skills break down. They named four things: turning information into good decisions, communicating and influencing with it, applying it to real work, and knowing what to trust. A fifth was confidence and culture, meaning fear of getting it wrong.

Nobody said their people couldn’t code. Every one of those is a judgment problem wearing a skills label, and the report’s own sentence on it is the most important line in all 37 pages:

“Without strong foundations, advanced tools (from dashboards and BI to AI systems) increase confidence without increasing correctness, which amplifies risk rather than reducing it.”

Certainty is what people act on, which is why the top risk these leaders named was inaccurate decision-making at 35%. It’s also why validating what AI hands you stopped being optional.

Here’s why one video can’t fix it. That video assumes everybody watching is the same person. When I assess how leaders relate to AI, two things matter independently: what somebody believes about it, and how much they actually use it.

That video is built for exactly one of them, the Eager Newcomer. Send it to a Skeptic and you’ve confirmed their suspicion that this is hype. Send it to a Reluctant Operator, already using AI daily while doubting whether they’re getting it right, and you’ve handed them nothing, because their problem was never access.

That Reluctant Operator is who this whole report describes. When people are unsure how outputs are governed, DataCamp found, they “either over-rely on outputs or avoid using tools altogether.” Both are judgment failures, and both look like adoption on a dashboard.

The five questions I ask before anyone buys anything

None of this requires a vendor, a budget approval, or a consultant.

1. How are our people actually being taught about AI? Not what got assigned, but what was learned.

2. What are the real mindsets about AI here? Not the ones people perform in the all-hands. The ones they say to each other afterward.

3. How is change management being handled? 30% of these leaders named resistance to adoption as a top barrier, which is a change problem showing up as a technology problem.

4. How is upskilling being handled? If it only reaches technical roles, most of your workforce is on their own.

5. How is any of this being communicated? Another 30% named unclear guardrails, which is a communication gap before it’s a capability gap.

Stop reading and try to answer number two honestly. Not what you’d put in a board update, what you actually know. If you can’t, that’s not a failure but the most useful thing you’ll learn all week. And it costs nothing: name AI champions on each team, or have your leadership team take the free AI Leadership Compass this week and compare results.

Now let me read this report the way I’m telling you to read your own data

I’d be a hypocrite if I spent this whole piece arguing that leaders can’t evaluate what they’re handed, then handed you a report without evaluating it.

DataCamp sells AI upskilling, so a report concluding that AI training drives returns is also their sales pitch. That doesn’t make it false, but it earns a harder look. The headline is a correlation, not proof, because companies organized enough to run company-wide upskilling are probably organized enough to deploy AI well too. 45% of respondents work in IT, so this is partly technology leaders grading everybody else. And the sample is four industries only, all at 500-plus employees, with no government, education, or nonprofits, so a 40-person mission-driven organization isn’t in it.

Credit where it’s earned: DataCamp refused to make year-over-year comparisons this edition because their methodology changed, and most vendor research compares anyway and hopes nobody checks.

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Focus on the people before you focus on the tool

The logistics company wasn’t being reckless. They’d had a tool fail and a reasonable theory that the next one would work better. What they were actually doing was deferring judgment to the tool, asking the software to decide whether the software would be adopted.

That’s the move I’d ask you to catch in yourself, because it’s easier to evaluate a product than to ask your own people what they believe. An organization can outsource its thinking just as easily as a person can.

Judgment is where my work starts, and you’re welcome to start there with me. You’re also welcome to do this yourself, and I mean that. Ask the five questions, find out which quadrant your people sit in, and write an AI policy a normal person can follow. Just don’t skip it and hope the tool is good enough, because that’s the bet the 79% who aren’t seeing real returns already made.

If You Only Remember This

• Every capability gap leaders named in this survey was about interpretation, communication, application, or trust, and not one was technical.

• Tools raise confidence faster than they raise correctness, so an unprepared team gets more certain and more wrong at the same time.

• A company-wide training video is built for one of the four kinds of people in your building, and the other three learn nothing from it.

Questions Leaders Are Asking

What is the AI skills gap? The AI skills gap is the distance between what organizations expect their people to do with AI and what those people can actually do. In DataCamp and YouGov’s February 2026 survey of 517 enterprise leaders, 59% reported an AI skills gap and 60% reported a data skills gap, even though most already offered some form of training.

What’s the difference between AI literacy and AI training? AI training teaches someone how a tool works. AI literacy is the ability to understand what AI can and can’t do, judge whether its output is reliable, and apply it to real work. Training covers the mechanics, and literacy covers the judgment, which is where the 2026 survey found every major gap.

Why isn’t our AI investment paying off? The strongest signal in the 2026 data is workforce readiness. Only 21% of leaders reported significant returns from AI overall, but among organizations with a mature, company-wide upskilling program that number rose to 42%. Worth noting this is a correlation, so upskilling may be a marker of organizational discipline as much as a direct cause.

How much does it cost to fix an AI skills gap? Less than most leaders assume, and often nothing to start. Naming AI champions on each team, running an internal survey on how people actually feel about AI, and writing a plain-language AI policy all cost time rather than money. Those three steps address change management, mindset, and guardrails, which is where this survey found the gaps.

Should we train everyone on AI or just technical roles? Everyone. In the 2026 survey, 38% of organizations trained only technical roles on data skills and 29% did the same for AI, yet the capabilities leaders said mattered most were decision-making and interpretation, which every role uses. Organizations with company-wide programs were roughly twice as likely to report significant AI returns.

Worth Your Time

A few pieces from other creators worth your attention this week.

• Kamil Banc — Two Years of AI Implementation Advisory: Seven Lessons. He watched two companies run identical AI stacks, one integrating it and the other flatlining by month three, and the difference was never the tool.

• Andreas Welsch — Why Your Eight-Week Roadmap Is Already Obsolete. A former SAP VP making the case that an AI roadmap older than eight weeks is already behind, and that light governance beats a longer plan.

• Oliver Patel — Banning AI Is Not an Effective AI Policy. A short, direct argument that restricting AI drives it underground instead of reducing risk, which is worth reading if question five was the hard one.

Sources Referenced

• DataCamp & YouGov, “The State of Data & AI Literacy 2026” (4th Edition), published February 26, 2026. Survey of 517 US and UK leaders at organizations with 500+ employees, fielded December 2025 to February 2026. Full report · Statistics summary

• Erik Brynjolfsson et al., “Generative AI at Work,” 2025, cited within the DataCamp report as the benchmark for a 15% productivity uplift among customer service workers.

• Kevin Cui, Princeton, cited within the DataCamp report for a 26% uplift in pull requests among software developers using Copilot.

Joel Salinas is an AI Strategy Coach and entrepreneur. AI is everywhere; judgment is scarce. Joel helps founders and leaders adopt AI without outsourcing their judgment to it, and he builds the systems too. Creator of the AI Leadership Triad.

Written by a human, for humans.

So I’ve done this more than once now. Because it’s so easy to create a tool or create an app, I’ve gone and built one, spent four or five hours on it, and replaced a task that generally takes me ten minutes once a month.

The app works. I still use it. And at that rate it has to keep running for more than two years before it’s paid for the afternoon it cost me. I never stopped in the middle of it to ask the only question that would have saved me:

is this a good use of my time, or is it just constant tinkering?

I didn’t have language for that until I sat down with Nir Eyal, who wrote Hooked, the book a generation of product teams used to build habit-forming apps, then wrote Indistractable. He handed me the test, and I’ve been failing it in a very specific way ever since.

Here is the breakdown.

What to Remember

• The one-line test that decides whether your last AI session was work or avoidance, and why it has nothing to do with what you were doing

• Why your to-do list is the wrong tool for catching this, and the one condition that has to be true before you drop yours

Distraction has almost nothing to do with the activity

Traction and distraction both come from the Latin trahere, to pull, and both end in the same six letters, action. So distraction isn’t something that happens to you while you’re minding your business, but a thing you did that pulled you away from what you’d already decided to do. Which gives you the test:

“Anything that is not what you said you were going to do in advance is by definition a distraction.”

The distractions that look like work are the dangerous ones

Here’s where it got uncomfortable, because Nir went straight at the respectable kind. He described sitting down with one big important task, deciding today’s the day, and then figuring he should probably check some emails first. Forty-five minutes later, what was I doing again?

“Just because something is a work-related task doesn’t mean it’s not a distraction. In fact, that’s the worst kind of distraction because you don’t even realize you’re distracted.”

His line for it: email seems to justify itself. I said on the recording that this was hitting close to home, and I meant it.

AI doesn’t just look like work. It produces work.

This is where I think the problem has gotten meaningfully worse since Indistractable came out in 2019, and it’s why I wanted to write this instead of just linking the conversation.

Email is a weak disguise. You spend forty-five minutes in the inbox and at the end you’re holding nothing, so sooner or later the emptiness gives it away. AI hands you an artifact. I walked away from those four hours with a working application, deployed, with a login screen on it. Every alarm I have for “you just burned an afternoon” is calibrated to look for an absence of output, and there wasn’t one.

The second version is quieter and I do it constantly as an author. When I’m writing a post, I could ask for feedback every second. I could ask for feedback 9,999 times, and then 9,999 times I would get feedback with detailed suggestions or improvements. There’s no point where the tool taps its watch and tells me the draft is done, and it will happily hand me a fourteenth angle on the same paragraph, and every one of those rounds feels like editing.

So it’s infinitely available, it never signals completion, and it produces evidence.

Think about it the same way I think about fire or electricity. Genuinely world-changing, and the same fire that heats your house is the one that burns it down. What decides which you get isn’t the fire, but whether you put it somewhere on purpose.

Ninety percent of it is coming from inside the house

The number I had to go back and check twice: we blame the pings, the dings, and the rings, and Nir puts those at roughly 10% of the problem. The other 90% is an internal trigger, which is really just a feeling. Loneliness, boredom, fatigue, uncertainty, anxiety. So when I open Claude to “just look at something,” most of the time I’m not chasing a notification. I’m avoiding the harder thing one tab over, and the tool is generous enough to make the avoidance look industrious. Then Nir closes the escape hatch:

“It’s not your fault. You didn’t create these digital technologies. But it is your responsibility because who else is responsible?”

Where should you actually start with AI?

We all got the tools at the same time, the way everyone eventually got electricity. Having it in the building was never what made the difference. What we did with it was.

I take three diagnostic calls a month. It’s 30 minutes, free, and we work out what you’re actually trying to get to, where your biggest hurdles sit, what your low-hanging fruit is, and how that lines up with what I’ve been seeing elsewhere. Then I tell you the first move I’d make.

A few days later you get it back in writing, two or three pages, yours whether we ever work together or not.

Book a diagnostic call →

Your to-do list can’t catch this

The obvious move is to write “don’t waste time in AI” on your to-do list and trust yourself. Nir’s argument is that the list is the broken instrument, and this is the part I didn’t want to hear, because I tend to live by mine. A list has no constraints, so you always add more and you always pick the easy item, and then you read back the leftovers night after night until you’ve built a convincing case against yourself.

“There’s nothing wrong with you. You’re not broken. It’s this stupid time management technique we call to-do list that’s broken.”

His replacement is timeboxing, which is just deciding in advance when you’ll do the thing, and it changes what counts as winning: did I work on what I said I was going to work on, for as long as I said I would, without distractions? Worth saying that David Allen made the case for Getting Things Done on this show in May, and the two of them do not agree.

For AI that’s the whole intervention. “Explore what Claude can do” is an invitation to lose a morning. “Forty minutes on the board deck, starting at nine” is a boundary the tool can’t argue with, and when the block ends you’ve learned something the list never tells you, which is how long the work actually takes.

Where I’d push back, gently

I don’t think Nir’s wrong anywhere. But I’d add a warning he doesn’t, because I can see how this goes sideways: dropping your to-do list without actually installing timeboxing is worse than keeping it. We’re built to be to-do list people, and a bad system beats no system by a wide margin. Don’t delete anything on Monday. Put one block on the calendar and see if you keep it.

There’s one more thing he said that’s aimed straight at people like me. Nir calls self-regulation the skill of the century, and then adds: “It’s not going to be, oh, I know how to use AI or I know how to code.” I’ve named three skills that separate leaders in the AI era, and I still think they’re the right three. Nir would tell me there’s a fourth sitting underneath. Here’s the clip.

Every night at eight, a little card locks my work apps, and getting back in means walking to my bedroom and tapping my phone against it. That walk is the entire point. Ten seconds of friction, usually enough to remind me that my eight-year-old is at the table and my three-year-old isn’t going to be three for very long. Nobody’s coming to regulate my calendar. Nir put it about as plainly as it can be put: you can’t make more time, and he doesn’t care if you’re Bill Gates or Elon Musk.

If You Only Remember This

• Anything you didn’t decide on in advance is a distraction, including the work-shaped ones. Email justifies itself, and AI justifies itself better, because it hands you something at the end.

• Nir puts roughly 90% of distraction on internal triggers rather than notifications, so the fix isn’t a stricter app blocker but noticing what you’re avoiding when you reach for the tool.

• Success stops being the checked box and becomes whether you worked on what you said you’d work on, for as long as you said, without wandering.

What’s the last thing you built with AI that you’d never have paid someone else to build for you?

Worth Your Time

A few pieces from other creators I think are worth your attention.

• Nick Woodford & — Before You Try Another AI Tool, Ask These 7 Questions. The filter I should have run before I built the app, including the best disqualifier in the set: if you’re still consciously deciding to open something three weeks in, it hasn’t earned its place.

• Frank Andrade — How to Use AI to Get Smarter (Not Just Work Faster). His confession that “AI was doing the work, and I was just collecting the output” is the same failure mode from the opposite direction.

• Cory Blumenfeld — You built something with AI that’s not a business. The sharpest line I read this month on where the freed-up time should actually go: everyone can build now, almost nobody can get the thing into the right hands. (I’ve written for Cory’s publication, so you should know that before you click.)

Questions Leaders Are Asking

Is using AI at work ever a distraction if it’s genuinely work? Yes, and that’s the hard part. Nir Eyal’s test is whether you decided on the activity in advance, not whether it appears on your job description. An unplanned hour inside Claude is a distraction for the same reason an unplanned hour in your inbox is, and it’s harder to spot because you finish holding something.

What is timeboxing, and how is it different from a to-do list? Timeboxing means putting a specific task in a specific slot on your calendar and working it for that whole slot. A to-do list records what you want to do without ever saying when, so it has no constraints and gives you no feedback. Timeboxing changes the measure of success from finishing to whether you worked without wandering.

How do I timebox AI work when I don’t know how long it’ll take? That’s the reason to start. Nir’s argument is that we take roughly three times longer than we estimate because nothing ever measures us. Book a 30-minute block, work it without interruption, and note how far you got. Two or three blocks in, your estimates get dramatically better.

Should I stop using my to-do list? Not on its own. Getting rid of your to-do list without putting timeboxing in its place leaves you worse off than you started. Keep the list as a capture tool, which Nir agrees you need, and move the deciding onto your calendar. Change one thing at a time.

Why do I keep opening AI tools when I have real work to do? Nir Eyal’s answer is that about 90% of distraction starts with an internal trigger, which is a feeling rather than a notification. Boredom, uncertainty, fatigue, anxiety. AI is unusually good at absorbing that feeling because it always responds and always produces something, so the avoidance never registers as avoidance.

About me

Joel Salinas is an AI Strategy Coach and entrepreneur. AI is everywhere; judgment is scarce. Joel helps founders and leaders adopt AI without outsourcing their judgment to it, and he builds the systems too. Creator of the AI Leadership Triad.

Written by a human, for humans.

The line I keep going over from Dr. Gleb’s piece is this one is that your company owns the licenses, but the vendor owns the practical knowledge.

I believe we’re in an era of building. And the sooner our people understand what building with AI actually looks like, the sooner they start wearing that hat, asking what’s possible now instead of staying stuck inside the box of what used to be possible. That shift is worth more than any single tool you’ll buy this year, which is part of why so many AI investments produce nothing while the license renews on schedule.

I want to complicate this before you read it, though, because this is the work I do for a living. So does Gleb, for what it’s worth, which means you’re about to get advice about outside help from two people who sell it. Read us accordingly. I’m not saying every employee should be writing code or unclogging it. Most of what an organization actually puts into production still needs professionals, because of the sheer volume of guardrails and security work it takes to make something genuinely useful instead of a high-risk hazard sitting inside your business. That part doesn’t go away, and anyone who tells you it does is selling something.

But understanding what’s buildable? That should be open to everybody. I’ve watched my seven-year-old build a working app in twelve minutes, so the ceiling is not where most of us think it is. Let’s open the gates, especially for the small and mid-sized organizations that keep getting told this stuff is out of reach.

is a behavioral scientist who was called the “Office Whisperer” by The New York Times, and he’s held faculty appointments at Ohio State and the University of North Carolina at Chapel Hill. His book The Psychology of AI Adoption at Work: From Resistance to Results comes out with Georgetown University Press in September 2026.

Here’s Gleb.

Stop Renting Your AI Capability: Teach Your Employees to Build AI Tools

Most organizations approach artificial intelligence as something they must buy from someone else.

A vendor demonstrates an impressive platform. A consulting firm proposes a custom solution. Leadership approves the contract, employees receive access, and everyone waits for transformation to happen.

Then the organization discovers the hidden cost. Every adjustment requires another vendor meeting. Every new workflow requires another statement of work. Employees understand the business problem but cannot modify the solution. The company owns licenses, yet the vendor owns the practical knowledge.

That arrangement may deliver a tool. It rarely creates internal AI capability.

The stronger alternative involves teaching employees to help design, build, test, and improve the AI tools they use. Leaders should still rely on technical experts for architecture, security, complex integrations, and high-risk systems. However, they should stop assuming that every useful AI solution must arrive as a finished product from an outside provider.

The Difference Between Buying Tools and Building Capability

Buying an AI tool solves a defined problem at a specific moment. Building capability changes how the organization responds to problems for years.

The distinction matters because AI tools and business needs evolve quickly. A vendor-built assistant may fit today’s workflow, but employees will soon identify exceptions, new applications, and better ways to use it. When only the vendor can make changes, the organization moves at the speed of procurement.

An organization with employee AI builders can move differently. Employees who understand customer complaints, compliance reviews, scheduling bottlenecks, or membership requests can turn proven prompts and workflows into repeatable internal assistants. They do not need to become software engineers. They need enough support, access, and governance to translate their expertise into useful tools.

This approach also changes the psychology of adoption. Employees experience AI as something they shape rather than something management imposes on them.

That shift creates ownership.

Why Domain Experts Often Build the Most Useful Tools

Outside developers may understand the technology better than your staff. Your staff understands the work better than outside developers.

The claims processor knows which parts of a file require judgment. The customer-service representative knows which questions waste the most time. The HR coordinator knows where onboarding repeatedly breaks down. The project manager knows which reports consume hours without improving decisions.

These employees possess what consultants often spend weeks trying to extract: operational knowledge.

Research on generative AI productivity illustrates why this knowledge matters. In a study of 5,179 customer-support agents, access to an AI assistant increased productivity by 14 percent on average, with much larger gains among less-experienced workers. The system helped spread practices associated with more capable employees.

That finding points toward a broader lesson. AI produces greater value when organizations capture internal expertise and make it reusable.

An employee-built intake assistant, reporting copilot, or policy guide can encode the organization’s actual language, standards, common exceptions, and review process. A generic vendor product usually starts farther away from that reality.

Start With the Task Employees Already Want to Eliminate

Leaders frequently begin AI programs with the use case that looks most strategic in a presentation. They should begin with the work employees already want changed.

Look for a recurring task that consumes time, follows a recognizable process, and still allows a person to review the result. Good early candidates include drafting routine communications, organizing meeting notes, comparing documents, preparing first-pass reports, categorizing requests, or answering questions from an approved knowledge base.

This focus creates a visible win without demanding immediate transformation.

It also supports stronger AI capability building. The World Economic Forum reports that 77 percent of surveyed employers plan to upskill workers in response to AI disruption. Yet generic training often produces knowledge that fades because employees never connect it to actual work.

Building one useful tool forces employees to apply what they learn. They must define the problem, identify the required information, test outputs, recognize failure modes, and decide where human review belongs.

That process develops judgment, not merely familiarity.

Move From Demonstration to Co-Creation

The first tool should usually involve expert support. The critical decision concerns how that support works.

A traditional provider disappears behind the curtain, builds the system, and returns with a finished product. An empowerment-oriented adviser builds in the open. Employees see how instructions get written, how knowledge gets organized, how output gets tested, and how safeguards get added.

The next tool should involve employees more directly. They propose the workflow, help configure the assistant, test realistic scenarios, and refine the results. With each iteration, the adviser does less and the internal team does more.

This graduated model turns users into AI champions. OpenAI’s 2025 enterprise report found rapid growth in the use of configurable GPTs and Projects, including organizations that developed thousands of internal assistants. The significance lies less in the volume than in the pattern: employees increasingly turn useful AI interactions into persistent tools embedded in recurring work.

Champions make that pattern spread. They translate between technical teams and business users, demonstrate credible examples, coach colleagues, and recognize when a proposed use case carries more risk than the team should handle alone.

Give Employees Guardrails Before Giving Them Freedom

Employee-built AI can create serious problems when organizations confuse empowerment with unrestricted access.

Staff should know which information they may enter, which systems they may connect, which tasks require approval, and which outputs demand human review. They also need a clear route for reporting errors or unexpected behavior.

The goal of AI governance should involve enabling responsible action rather than preventing all experimentation. The National Institute of Standards and Technology’s generative AI risk profile gives organizations a structured way to consider risks throughout the design, deployment, use, and evaluation of AI systems.

Leaders can translate that broad framework into practical internal rules:

• Employees may build only within approved platforms and accounts

• Sensitive, confidential, or regulated information requires specified protections

• Every tool must have a named owner

• Teams must test the tool against realistic and adversarial scenarios

• High-impact decisions require human review

• Widely used tools require periodic evaluation and documentation

Organizations also need visibility into what employees build. Microsoft’s current Power Platform governance guidance describes digital guardrails that allow professional and citizen developers to create solutions while administrators maintain security, compliance, and oversight.

The principle applies across platforms. Centralize standards and monitoring while distributing experimentation and problem-solving.

Build a Small Internal System Around the Builders

A sustainable program needs more than a few enthusiastic employees.

Create a small enablement group that includes business champions, IT, security, legal or compliance when relevant, and an executive sponsor. This group should approve platforms, publish simple standards, review higher-risk tools, maintain reusable templates, and help builders solve problems.

Large organizations may call this a Center of Excellence. Smaller organizations can accomplish the same purpose with a monthly review group and a shared library.

The structure should remain light enough to support experimentation. Microsoft’s citizen-development model at Deutsche Bahn combines central standards with local expert teams that coach builders and review solutions before deployment. That model avoids forcing one central team to approve every minor decision while preserving accountability.

The enablement group should also track outcomes. Count fewer things, but choose measures that matter: hours saved, cycle time, errors, rework, employee use, customer response time, and the percentage of tools that remain useful after several months.

The number of tools built means little if employees abandon them.

Recognize Where Employee Building Should Stop

The empowerment approach does not mean employees should build every system.

Professional developers and specialized vendors remain essential when tools involve complex integrations, sensitive data, critical infrastructure, high-stakes decisions, or requirements for extensive reliability and scale. Citizen builders should not quietly construct systems that approve loans, make medical recommendations, determine employment outcomes, or bypass established controls.

Employees should own problem discovery and remain deeply involved in design and testing. Technical experts should own the elements that require specialized engineering and risk management.

The OECD’s workplace research found that worker consultation and training correlate with better outcomes for workers. Consultation does not require handing employees unrestricted authority. It means involving them meaningfully in decisions that reshape their work.

This hybrid model avoids two bad extremes: total vendor dependence and uncontrolled shadow development.

Measure Success by How Much the Organization Learns

The first internally built AI tool may save only a few hours each week. Its larger value comes from what the team learns while creating it.

Employees learn how to describe a workflow precisely. Managers learn where policies contain ambiguity. IT learns which approved capabilities employees actually need. Leaders learn which teams have credible champions. The organization begins building a shared vocabulary for testing, risk, human review, and value.

That learning compounds.

The OECD found that many workers report better performance and greater enjoyment when AI improves their work, while also expressing concerns about privacy, work intensity, and inequality. Leaders should take both findings seriously. Employees will support AI more readily when it removes genuine friction and when they can influence how it enters their work.

That principle sits at the heart of effective AI adoption at work. People support what they help create, especially when leaders give them useful tools, clear boundaries, and visible responsibility.

The strongest AI strategy therefore asks a different question.

Do not ask only, “Which AI system should we buy?”

Ask, “What must our people learn to build, improve, and govern for themselves?”

Vendors can supply technology. Consultants can accelerate the journey. Neither should own your organization’s ability to innovate.

Your long-term advantage will come from employees who understand the work, understand enough about AI to improve it, and possess the authority to turn their knowledge into better ways of operating.

That is capability you own.

Thank you, Dr. Gleb!

The last line is the one to sit with, because capability you own is the only kind that survives a vendor changing their pricing, their roadmap, or their mind. The gate isn’t the tooling anymore, it’s whether your people know what’s on the other side of it, and that’s a question you can start answering this week without a procurement cycle.

His book, The Psychology of AI Adoption at Work: From Resistance to Results, comes out with Georgetown University Press in September 2026.

What’s one thing someone on your team wishes existed but assumes has to be bought? Tell me in the comments, because I’d guess a good number of them are closer than they look.

Questions Leaders Are Asking

Should we build or buy AI tools? Buy the platform, build on top of it. Almost nobody should be building foundation models or core infrastructure. What you build are the configured assistants, workflows, and knowledge layers that sit on a vendor platform and encode how your organization actually works. That’s where the capability lives.

Do employees need to know how to code to build AI tools? No. Most employee-built AI tools are configured, not programmed: instructions, uploaded knowledge, defined steps, and tested outputs inside an approved platform. The skill that matters is describing a workflow precisely and knowing where a human has to check the result. Anything requiring real engineering should go to professionals.

What are the risks of letting employees build their own AI tools? Data exposure, unreviewed outputs reaching customers, tools with no owner when the builder leaves, and quiet duplication of systems IT already runs. Every one of those is manageable with approved platforms, named owners, defined data rules, and human review on high-impact decisions. Set those before you open access, not after.

How do we start an employee AI building program? Pick one recurring task your team already wants to eliminate, build it in the open with expert support so people see how it’s done, then hand more of the next one to them. Add a small review group with IT, security, and a business champion. One useful tool beats a training program nobody applies.

What is a citizen developer? A citizen developer is an employee who builds working applications or automations without being a professional software developer, using approved low-code or AI platforms under an organization’s governance rules. Deutsche Bahn runs roughly 11,000 of them with local expert teams coaching and approving their work before release.

How do we measure whether employee-built AI tools are working? Track hours saved, cycle time, error and rework rates, and how quickly customers get answers. Then track the number that matters most: what percentage of the tools are still being used six months later. Counting tools built tells you about enthusiasm. Counting tools still in use tells you about capability.

About the Author

Dr. Gleb Tsipursky, PhD, is a behavioral scientist called the “Office Whisperer” by The New York Times. He serves as CEO of the future-of-work consultancy Disaster Avoidance Experts, and his academic career includes faculty appointments at Ohio State University and the University of North Carolina at Chapel Hill. His book The Psychology of AI Adoption at Work: From Resistance to Results is out with Georgetown University Press in September 2026.

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

What You Can Expect

• A test you can run on your own next campaign: are you orchestrating what reaches a customer, or letting four departments hit the same person independently?

• The one question Dave asks every company he walks into, and why most of us can’t answer it honestly.

🎥 Catch all future interviews, subscribe!

🎧 Prefer audio? Listen on Apple Podcasts

Outline

(00:35) – Marketing since 1986, and riding every wave since

(03:55) – Why he wrote the book: slop was the danger

(07:35) – The solar mailer: Google Earth, Zillow, and 21.3%

(11:55) – Test and learn, and how Sungevity actually got there

(13:52) – Zero-party data, and the companies that never just ask

(17:15) – The front desk secretary who tested every new tool

(18:28) – Morgan Stanley, and ten hours a week back

(22:31) – One customer, four departments, all bombarding

(25:45) – Where trust turns into feeling watched

(28:26) – Why this is a strategy decision before it’s a marketing one

(31:15) – Call center transcripts, and catching a bug in fifteen minutes

(32:11) – The one thing: the high road

My Takeaways

The junk mail that worked. Dave’s town offered a property tax discount for solar panels, which set off an avalanche of mail. One envelope did something the others didn’t. He typed in a personalized URL and got a Google Earth image of his own roof with panels drawn onto it, sized to his roof’s orientation and latitude, with his likely annual usage estimated from the square footage on Zillow.

“So they had the numerator from Google Earth, the denominator from Zillow. It came to 21.3%. They said they would guarantee 20%.” — Dave Edelman

He bought without looking at a competitor, stayed seven years, and cleared 20% every year but one, when they sent a rebate check instead. And you don’t get there without 25 iterations, but it started with thinking, how can we truly delight our customers?

Delight is where almost everyone stops. The book runs on five promises: empower me, know me, reach me, show me, delight me. I don’t think we normally get to the delight area, it usually stops way before that, and the delight-me ones are exactly the ones that hold customer trust for years. What I hadn’t thought about is that AI is what finally makes that last promise affordable, because the testing that gets you there also feeds the thing doing the testing.

“It also feeds your AI. You get more data from the tests that make that AI smarter.” — Dave Edelman

Orchestrate, or bombard. One retailer ran every department on its own marketing budget, and the best customers of one category turned out to be the best customers of most of the others, so the same people got hit from every direction at once. Response rates fell.

“The customer isn’t a customer of a category, a department. Customer is a customer of our brand overall.” — Dave Edelman

They pooled the data, used AI to rank the top three things any given person would actually care about, and governed every message down to those three. Loyalty memberships up, sales up over 20% a year, cited at their investor day. To me that tells the customer we value your attention, which is the same line I’ve drawn before on when personalization crosses it.

It’s a strategy decision before it’s a marketing one.

“The first step is, this is a strategy decision. This is about how you want to compete. And that’s a real strategic decision, to restrain yourself in order to get a higher response rate.” — Dave Edelman

I coach CEOs on AI strategy, and time and time again I see the left hand of an organization with no idea what the right hand is doing, both of them touching the same customer, sometimes to the detriment of the other. And the customers just don’t feel known. Now, every company Dave named is enormous, so the fair question for the rest of us is what this looks like at forty people. His cheapest fix scales all the way down: poll your own employees first. Which reminded me of a CEO I worked with who had his front desk secretary, about 55 and not remotely techie, walk every new sign-up flow before launch and report back to marketing. It’s not hard, but it’s so rare. Same gap I keep running into when AI initiatives stall.

One Question Before You Go

Dave’s own test is the one I’d carry out of this hour.

He calls the first one the high road, and he’s watched it build brands since 1986.

So put yourself in the shoes of your customer, of your audience. Would they like what you’re doing, would they trust you, and would they be delighted?

Watch the full conversation above, and go follow Dave on LinkedIn, where he publishes on this constantly.

About David Edelman

David C. Edelman teaches marketing at Harvard Business School and advises CEOs and CMOs through Edelman Advisory Services. He was Chief Marketing Officer at Aetna, co-led McKinsey’s global digital marketing and sales practice for eight years, and was an EVP at Digitas and a partner at BCG before that. Personalized: Customer Strategy in the Age of AI, co-authored with BCG’s Mark Abraham, came out from Harvard Business Review Press in October 2024. More than a million people follow his writing on LinkedIn.

About me

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

In late July I ran three things through Substack’s AI detector.

The first was something I wrote entirely myself, with no AI anywhere in it. It came back 100% human, which is what I expected.

The second was text I copied straight out of Claude and pasted in without touching a word of it. That one came back 50%.

The third was a transcript of me talking in a meeting. My own voice, my own words, recorded and typed up. The detector said it was 100% AI.

Honestly, after that third result I stopped treating the number as information.

Which matters a lot more this week, because Anthropic just started weaving an invisible watermark into everything Claude writes, and European law is about to make that standard across the industry. Before long, most AI-assisted work you come across will carry a hidden mark saying a model touched it, and a lot of people are going to treat that mark as a verdict on whether the work is any good.

My take probably isn’t the one you’d expect from someone who publishes AI-assisted writing twice a week. The watermark is fine. I’m glad it exists, and I’ll tell you why. But it answers a question that’s on its way out, and the question that actually decides whether your work is worth anything is one that no watermark and no detector will ever be able to answer.

What You Can Expect

• The line in Anthropic’s own documentation that decides what a watermark is worth to you

• A ten-second check to run on anything before it goes out under your name

What Anthropic actually shipped

Anthropic, the AI company behind Claude (the main rival to ChatGPT), confirmed in an update to its own support pages, first reported August 11, 2026, that it’s weaving an invisible watermark into the text its models generate. A watermark here means a machine-readable pattern hidden inside the words themselves, not a visible label. It applies everywhere Claude is offered, worldwide, and the driver is the EU AI Act’s transparency rules, in force since August 2. Google already marks Gemini’s text and OpenAI has signed the same code, so this stops being news very quickly.

Here’s the part I’d want you to read before forming an opinion, and it comes from Anthropic’s own support page, not from a critic:

“Claude may not be the original author. People often use Claude to proofread, translate, summarize, or convert files. The output can carry a Claude mark even if the underlying ideas, text, or data originated from another source.”

So a paragraph you wrote yourself, ran through Claude to fix the grammar, and published under your name carries the mark.

Substack said close to the same when it added AI detection through a company called Pangram in July. CEO Chris Best wrote in the announcement that the tool “can only detect whether AI was used to make the text, not whether great human care went into creating it.”

Both companies shipped the detection and published the disclaimer in the same breath.

Why I’m not against any of this

I understand the need for people to have something they can trust. People have lied to each other for as long as there have been people, and we still trust people far more than we trust AI. That isn’t irrational, and if I’m giving someone my attention or my money I want to know a person was on the other end of it, so I see exactly why these tools are showing up now.

But we’re collapsing two different questions into one, and they’re worth pulling apart. Call them the two questions, because every argument about AI and trust is really one or the other.

Today’s question is “was this made by AI?” That’s a fear question, and the watermark answers it about as well as anything can.

Tomorrow’s question is “is there anything of you in this?” No watermark answers that one. No detector does either. My meeting transcript is the proof, because a machine looked at a recording of me talking and called it artificial.

Everyone can buy the same guitar

I play guitar. Anyone reading this can buy the same guitar I own and download the same sheet music I use, and none of that is the interesting part. What makes it mine is that I have things I like doing, my own take on different songs, and I’ll put them in whenever I feel like it regardless of what the exact sheet music says. The instrument was never the difference, it is everything I added to the music sheet.

That’s what I’d want us to be honest about, because the risk was never that copy-pasted AI output is bad. It’s good, usually better than the rushed draft we’d have written ourselves at 11pm, and it improves every few months. The risk is that it’s good in exactly the way it’s good for everybody else running the same tool.

Millions of people have access to the same model you do, so if there’s nothing of you in what you publish, you’ve handed away the only thing you had that was scarce.

No detector has to catch you for that to cost you.

This is what I tell the leaders I coach too. A company doesn’t get ahead because it adopted AI, because everybody adopted AI. It gets ahead when its people put real expertise in and use AI strategically on top of that. That’s the real reason outsourcing your thinking to it costs you, and it’s where we ended up after testing eight AI humanizers for twenty hours and watching most of them fail.

How I actually make this newsletter

Since it’s fair to ask, here’s my process, unedited:

I start by researching a topic extensively. I feed that research into AI to synthesize it into a single reference document. I study that document, then dictate my thoughts into a long-form transcript. That transcript goes through Claude for an initial cleanup pass, adding headlines and an outline. From there, I take the draft through manual editing to produce the final post.

Set that against this week’s news and two things are true at once. The synthesis step and the cleanup pass would carry Anthropic’s watermark, and a detector would very likely score this piece as AI-assisted. Neither of those facts tells you that the research, the thinking, the dictation and every editing decision were mine. That’s the gap, and a better detector doesn’t close it.

The check to run before anything goes out

Before something goes out under your name, to your readers, your customers, or your board, point at the specific thing in it that only you could have written. A judgment call you made, a number from your own operation, a position you’d defend in a room full of people who disagreed, something you got wrong once and learned from.

If you can’t point at it, it isn’t ready. That’s the whole test, and it costs nothing to run.

Don’t focus on the tool. Don’t focus on the watermark. Focus on how you’re using AI, and on the judgment you’re bringing to it, because that was always the only part of this that was yours.

If You Only Remember This

• There are two questions, and only one of them matters in a year. “Was this made by AI?” is what the tools answer. “Is there anything of you in this?” is what your readers are actually deciding.

• Copy-pasted AI output isn’t risky because it’s bad. It’s risky because it’s good in the identical way for the millions of people running the same model.

• Human expertise with AI used strategically on top is what puts a person or a company ahead. AI by itself moves everyone toward the same average.

What’s the part of your work that a model couldn’t produce even if you handed it your entire archive? I read every reply.

Worth Your Time

A few pieces from other creators I think are worth your attention.

• Kamil Banc — Your next business decision might already have an AI mistake in it. His line that clients aren’t paying for information but for someone who already checked it is the cleanest version of this whole argument I’ve read.

• Oliver Patel — Banning AI is not an effective AI policy. The case for governing how your people use AI instead of whether they use it, from someone who reads the EU AI Act for a living.

• Andreas Welsch — Your Anonymous AI Usage Is Coming To An End. Written in June, and it called the traceability wave that Anthropic’s watermark is now part of.

Questions Leaders Are Asking

What is Anthropic’s AI watermark? It’s an invisible, machine-readable pattern embedded directly into text that Claude generates, plus signed provenance data attached to generated files. Anthropic says it applies to models launched on or after August 2, 2026, across every Claude product, worldwide, and that it’s being added to older models.

Does a watermark prove something was written by AI? No. Anthropic’s own documentation states that a detected mark shows content was processed by Claude and “is not fully conclusive.” Text you wrote yourself and ran through Claude for proofreading or translation can carry the mark. Anthropic also says the absence of a mark doesn’t prove content is human-written.

Can the watermark be removed by editing? Anthropic says the mark travels with copied and pasted text and “may persist through some editing,” and that heavy rewriting, translation, mixing with other text, or passages that are too short can make it undetectable. The company has not published how much editing removes it.

Does Substack label my posts as AI now? Not automatically. Substack added an AI detection scan in July 2026 through a company called Pangram, and it runs on posts and notes over 100 words published on or after July 21, 2026. A reader or writer has to request the scan, the result shows only to whoever requested it, writers can run it on drafts, and writers can disable detection on their own posts.

How accurate is AI detection? Better than it used to be, and still not something to make decisions on alone. Pangram publishes a false-positive rate of roughly one in ten thousand. Princeton’s Arvind Narayanan has pointed out that even at that rate, running a student’s 500 to 1,000 assignments through a detector across a four-year degree would falsely accuse 5 to 10 percent of a student body at some point. Detectors also measure process rather than quality, so a careful piece written with AI help and a lazy one written by hand can score in ways that invert their actual worth.

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Affiliate links included.

Written by a human, for humans.

I’ve been investing for fifteen years. Not casually, and not with money I could afford to shrug at, because a large part of my income has gone into the market that whole time. That’s a long enough run to have made most of the mistakes at least once, and I have made many.

When I came across ’s work, what got my attention wasn’t the AI angle, but that his investing ethos is almost exactly mine, and he happens to be an actual expert in a field where most of what gets written is either hype or fear. He runs a real-money portfolio, he publishes every position and every mistake in public, and he’s blunt about what AI can and can’t do with your savings.

I’ve written before about why you shouldn’t outsource your thinking, even to AI, and about what it means when your AI never disagrees with you. Jing is doing that same work with his own capital on the line, which is a harder test than any of us run on a Claude prompt, and it’s why I wanted the leaders I serve to read him.

One thing to set up front. Jing wrote this in July 2026, so the market numbers in it are a snapshot of that week. Neither of us is publishing a call on where stocks go next. Read it for the process, which holds up whether the market runs or breaks.

Here’s his piece. I’ll meet you at the end.

Before you read:

• Nothing here is investment advice. It’s one person’s process, described in public, so you can build your own.

• If you don’t invest, read it as a delegation case study. The tickers are the vehicle; the boundary is the lesson.

• The question isn’t what to buy today, but what you’ll do when everything goes on sale.

• AI made research nearly free. It did not make judgment optional.

Investing Advice for the Age of AI

Hint: it starts with cash and a list

Everyone who finds out I run an AI-powered investment portfolio asks me the same question: “What should I buy?”

It’s the wrong question, and it’s wrong in a specific way. Today the S&P 500 trades at a CAPE ratio just above 40 — a mouthful of a metric that just means stocks are more expensive, relative to a decade of earnings, than at almost any point in market history. In 145 years of data, only one stretch was pricier: the dot-com peak of 1999–2000. Even 1929 and 2021 sat below today’s level. You may remember how the years that followed those peaks felt.

I’m not predicting a crash — and I say that as someone who once tried to. After the market sold off in December 2018 and then V-shaped straight back up, I was convinced the recovery couldn’t hold. So I shorted the S&P 500, and I stayed short for essentially all of 2019 while the market ground higher against me. That bet cost my portfolio about 15% while I waited to be proven right. The crash did eventually come — in March 2020, more than a year later, triggered by a pandemic nobody could have named in advance. I covered the short and spent the following weeks buying stocks at historically cheap multiples, so the story ends well on paper. But I’m honest with myself about what actually happened: I had the direction right, my timing was wrong by over a year, and luck bailed out the difference. That trade taught me the lesson this entire essay is built on. Knowing the market is expensive tells you nothing about when it breaks. Betting on when is how you lose money being right. Preparing for eventually is how you make it.

You don’t need to take the extreme route I did. History says it plainly: since 1950, the S&P 500 has fallen 20% or more from its high eleven separate times — roughly once every seven years, through wars, recessions, panics, and pandemics. It will do it again. The date is unknowable. The event is close to certain.

Honesty also requires acknowledging the other side of this market. The AI trade has made some people genuinely rich — the ones who were early in Nvidia below a split-adjusted $100, or in memory stocks like Micron when it traded under $100 a share. And after the brutal correction of the past few weeks — Micron cut from a high around $1,200 in late June to the low $800s, SanDisk from over $2,300 to near $1,100 — these names can absolutely still run further. But be clear-eyed about what this is: a trader’s market. The early birds have largely cashed out or sit on a cost basis that’s a fraction of today’s price; if you’re buying now, you’re accepting real upside potential and equally real downside risk, without their margin of safety. There’s nothing wrong with taking that bet — just know that at these levels it’s a trade, not necessarily a long-term investment, and don’t confuse it with the strategy in this essay.

So the right question isn’t “what should I buy today?” It’s: “what will I do when everything goes on sale?”

For the average person — no Bloomberg terminal, no research team, a day job — the honest answer has two parts. Neither requires genius. Both require doing something now, while the market is calm and expensive.

Part one: build the cash pile

When stocks are expensive, the best-performing move available to most people is boring: accumulate cash.

Not cash under a mattress — cash in Treasury bills, currently yielding about 3.75%, or a money-market fund, where the big-name funds pay in the 3.3–3.6% range. That’s your baseline. Every stock you buy has to beat it to be worth the risk. At today’s valuations, a lot of stocks won’t.

Cash gets a bad reputation because it “does nothing.” I’d put it differently: cash is an option on every future bargain, and right now that option pays you roughly 3.5% a year to hold it. In our portfolio, roughly half the book sits in cash and T-bills — deliberately, and we’ve said so publicly every quarter. In an expensive market, patience is a position.

Here’s the line I’d ask you to remember: when the correction comes, cash is the difference between being a buyer and being a seller. The people who got wealthy buying in March 2009 or March 2020 weren’t smarter that month. They just arrived with cash while everyone else arrived with margin calls.

And AI can help you build the pile, not just deploy it. Nothing fancy — three practical moves: export a few months of bank and card statements and ask an AI assistant to categorize your spending and flag every recurring charge you forgot about (subscriptions are where cash piles go to die). Tell it your income and fixed costs and have it propose a monthly auto-transfer into T-bills you’ll actually stick to. Then once a quarter, have it recompute your savings rate and project the date your war chest hits its target. Budgeting fails on tedium, and tedium is exactly what AI is good at absorbing. The saving still has to be automatic — AI just makes the plan honest and keeps it current.

Part two: build the shopping list — stocks you want when they go on sale

Cash alone isn’t preparedness. Plenty of people held cash through 2020’s crash and never bought anything, because when prices finally broke, fear made every stock look broken too. At the bottom, every headline is a reason to wait.

The antidote is a shopping list written before the panic: five to fifteen businesses you understand, would be proud to own for a decade, and have researched enough to name a price you’d pay. Not tickers your cousin mentioned. Businesses whose products you can explain, whose filings you’ve at least skimmed, whose risks you can say out loud.

If you’re not sure how to build that list, you have two good options. The lightweight one: ask any AI assistant to help you build a simple screen — say, consistently profitable businesses with low debt and high returns on capital — and use it to generate candidates you then research one by one. The deeper one: I’ve open-sourced the entire system I use to run our own portfolio. It’s called Clarion Intelligence System, it’s free, and it’s built for anyone willing to roll up their sleeves. A similar version of the same system manages our own internal capital at Elendil Labs — you can inspect the live Elendil portfolio, every position and thesis included, to see exactly what it produces. It runs on Zo Computer, a managed personal AI server — an always-on assistant with its own computer, so the research keeps running when your laptop is closed. Setup is a sentence: install the skills and tell the agent to “set up Clarion.” From there, “evaluate Costco” pulls the SEC filings, scores the business on quality and value, and drafts the research file — including the two numbers this essay keeps insisting on: the price you’d pay, and the facts that would prove you wrong.

Then — and this is the step that separates preparation from daydreaming — write the price down and stage the order.

A story from our own book. In May, we decided Meta was worth owning below $580 and Microsoft below $400. Prices were higher, so we placed standing limit orders and went back to work. Weeks later, in a June pullback, both orders filled — at exactly the prices we’d chosen when we were calm. On the day it happened, no decision was required. The decision had been made weeks earlier, by a version of us that wasn’t watching a red screen.

Note what those fills were not: a signal to go all-in. We still hold roughly half the book in cash and Treasuries, because the market as a whole is still expensive and we want dry powder for better opportunities ahead. But that pullback in two of the largest companies on Earth makes the point — even in an expensive market, individual businesses go on sale for days at a time, and the only people who catch those windows are the ones who wrote down an entry price and a view of fair value in advance.

That’s the whole trick. Decisions made in calm, executed in panic — automatically. Your future panicking self is not the person you want picking entry points. Your current, unhurried self is.

Where AI actually fits in

Now the part everyone expects me to say, since I build AI systems for a living: no, AI will not pick winning stocks for you. If someone sells you that, keep your hand on your wallet.

What AI genuinely changed is the cost of doing the homework. The research that used to require a team — reading hundreds of pages of SEC filings, screening thousands of companies, checking whether insiders are buying, monitoring every position for the specific facts that would break your thesis — is now available to one person with a laptop. I run our entire book this way. AI agents index the filings, screen for quality and value, track a market-regime signal, and re-check every position’s health on a schedule. What took my former research colleagues a week takes the system an afternoon.

Three examples of what that looks like in practice:

AI reads what screeners miss. Last spring, a stock screener scored IBM near the bottom of our list — its decades of share buybacks had distorted a standard debt metric so badly the screen read it as drowning in leverage. The actual 10-K told a different story. The system flagged the discrepancy because it reads filings, not just ratios. A number can lie; a filing lies less.

AI finds the needle. The Trade Desk’s founder-CEO bought $148 million of his own stock at $24.71 — disclosed in a Form 4 filing most people will never open. Our pipeline surfaces insider buying like that automatically. We later bought at $20.33, seventeen percent below where the CEO put his own money at risk. That’s not forecasting. That’s reading — at machine speed.

AI never gets tired of checking. Every thesis we hold has written “kill conditions” — specific, testable facts that would mean we’re wrong. The system checks them continuously against live data. Humans get attached to positions; a checklist doesn’t.

And one example of the opposite, because honesty matters more than marketing: our monitoring system once flagged five positions as urgent sells. Every one was a false alarm — the underlying data fields were empty, and the system had averaged zeros into failing scores. We caught it because every AI output in our shop is treated as a draft to verify, not a verdict to obey.

So here’s the honest version of the human-versus-AI question, without a slogan. In our process, AI’s role is enormous and still growing: it does nearly all the reading, the screening, the monitoring, the trade research, and the operational prep. But in the portfolio we publish, a human reviews every recommendation and a human places every trade and limit order. That’s not a permanent ceiling on what AI can do — agentic systems earn more autonomy every quarter, ours included. It’s how trust is supposed to work: let AI assist everything, and let it own only what it has repeatedly proven it gets right. The frontier of what you delegate should expand with its track record — the same standard you’d hold a new analyst to. For most people starting out, that means AI dramatically accelerates the research, the checking, and the ops, while the decisions that put your savings at risk — what to own, how much, at what price — stay with you. In our shop that boundary is written down, along with a deployment checklist every trade must pass. The checklist took fifteen minutes to write. The mistakes it prevents are measured in thousands.

The advice, in one place

If you’re starting out, in this market, here’s what I’d actually do:

1. Automate saving into a T-bill or money-market fund. This is your war chest. ETFs like SGOV make it easy: they’re liquid, the share price barely moves, and they currently yield about 3.6% while you wait — so the money is stable and ready the day you need it. Stop feeling guilty about holding cash.

2. Build a list of 5–15 businesses you understand and would hold for ten years. Use AI to help you read the filings and earnings calls — that’s the legitimate shortcut.

3. Borrow a system instead of building one from scratch. The Clarion Intelligence System is open source, and on Zo Computer it runs in plain English: “set up Clarion,” then “evaluate [company],” then “monitor my theses.” The filings get read, the scores get computed, and the checking happens on a schedule — whether or not you’re at your desk.

4. Write down the price you’d pay for each. If you can’t name a price, you haven’t finished the research. AI can help you get there.

5. Write down what would make you wrong. One sentence per stock. If you can’t write it, you don’t understand the business yet. AI is genuinely good at this one — feed it a company’s last few 10-K and 10-Q filings and ask what facts would break the investment case.

6. Stage standing limit orders where your broker allows. Let calm-you make the decision and panic-day markets fill it.

7. Stop trying to time the top. You only need to be ready for the bottom, and readiness is a checklist, not a feeling.

The uncomfortable truth about investing in the AI age is that the machines have made research nearly free, but they haven’t changed the thing that actually determines results: whether you show up to the correction with cash, a list, and prices you chose in advance — or with regret.

And if you’d rather see all of this in practice than take my word for it, everything we do is public: the live Elendil portfolio and every thesis page at cis.zo.space, and the running investor letter — wins, mistakes, and lessons included — at cis.zo.space/letter. Copy whatever is useful.

And if you work — or want to work — at the intersection of capital markets and AI, that’s exactly what we write about. Subscribe to the Elendil Labs Substack at elendillabs.substack.com for ongoing investment research and posts like this one.

Thank you, Jing!

Elendil Labs

Perspectives on AI, institutional memory, and the future of capital-markets work.

By Jing Xie

AI isn’t a binary where you’re either all in or all out, but a set of choices about what you hand over and what you keep, and the leaders getting real value out of it are the ones making those choices deliberately instead of by default. Adaptability, one of the three skills in the AI Leadership Triad, is mostly this in practice: knowing what to move and what to hold.

Whichever way you draw the line, you’re 100% responsible for the decision, whether you pulled the trigger or your AI did.

Where have you drawn your line, and did you decide it on purpose or just end up there?

Jing writes at Elendil Labs. If capital markets and AI is your intersection, subscribe to him there.

Questions Leaders Are Asking

Can AI pick stocks for me? No, and Jing is direct about it. AI in a real-money portfolio does the reading, the screening, and the monitoring. A human still decides what to own, how much, and at what price. Any tool sold as a stock picker is selling you certainty that doesn’t exist.

How do I decide what to let AI do and what to keep? Write the boundary down before you need it. Let AI assist with everything, and let it own only the tasks it has repeatedly proven it gets right. Expand that list as the track record grows, the same way you’d expand what a new hire handles.

Do I need to be technical to use AI for research? No. Jing’s system runs on plain-English instructions like “evaluate Costco.” The harder skill isn’t technical, but knowing what question to ask and how to check the answer. If you can read a summary skeptically, you can do this.

What’s the most common way this goes wrong? Treating AI output as a verdict instead of a draft. Jing’s monitoring system once flagged five positions as urgent sells and every one was a false alarm caused by empty data fields. They caught it because verification was already part of the process, not an afterthought.

Does this apply outside investing? Directly. Any decision with real consequences has the same structure: research that AI can accelerate, and a judgment call that stays yours. Hiring, budgets, vendor selection, and strategy all follow the same pattern of automating the reading and keeping the deciding.

What AI tool should I start with for this? Start with whichever assistant you already use, because the discipline matters more than the tool. Ask it to summarize a document, then verify one claim against the source yourself. That single habit of checking is the whole method, and it transfers to every tool you’ll use later.

About the Authors

Jing Xie runs a real-money, AI-native investment portfolio and publishes every position, trade, and mistake at cis.zo.space. He writes about capital markets and AI at Elendil Labs. Nothing here is investment advice, but a description of a process, offered so you can build your own.

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Disclaimer: Nothing in this essay is investment advice, an offer, or a recommendation to buy or sell any security. It describes one process for educational purposes. Investing involves risk, including the possible loss of principal. Past performance does not guarantee future results. Do your own research and consult a licensed financial professional before making investment decisions.

Written by a human, for humans.

You can drive a nail with a wrench. It works, mostly. The nail goes in, the wrench doesn’t break, and you walk away telling yourself you got the job done. Then one day you pick up an actual hammer, drive the same nail in half the swings, and feel how much effort you’d been wasting the whole time. The wrench was never the problem. You were just using it for a job it wasn’t built for.

That’s most of us with AI right now.

We pick one tool, usually whatever we opened first, and we make it do everything. Research, drafting, analysis, summarizing a stack of videos, building a board deck, all of it through the same tab. And it kind of works. We’re hitting the target every so often, the way you hit a target if you shoot enough arrows at it. But there’s a better way to spend the energy, and it starts with a distinction almost nobody makes.

Here’s the thing. We’re past the point where using AI is the advantage.

Everybody’s using it now. Your competitor is using it, your team is using it, the person across the table in your next meeting is using it. The edge moved. Using AI used to set you apart, and now that everyone’s doing it, the thing that sets you apart is using it well. That starts with knowing there are two different kinds of AI sitting on your desk, built for opposite jobs, and knowing which one to reach for.

That’s what this post is: the whole decision, laid out plainly. What the two engines are, how they’re different, and exactly when to use each one. It’s short on purpose, because this is the part your whole team needs to get right, not just you, so send it to them. The step-by-step of actually running the harder engine, I put into a field guide I’ll point you to at the end.

Stay until the end for your copy of my NotebookLM’s Leader Playbook… 📕

In this post, you’ll learn:

• The two kinds of AI on your desk, and the one difference that actually matters

• A simple rule for knowing

  which one to reach for before you type a word

• A 30-second checklist you and your team can run on any task

The two engines, and the one difference that matters

Most of us treat AI as one thing you’re either good at or you’re not. That framing is the whole problem, because the tools split into two very different jobs, and once you see the split you can’t unsee it.

• The grounded engine. This is NotebookLM (Google’s AI research tool that only answers from the sources you personally give it). You hand it your documents, your PDFs, your spreadsheets, even a pile of YouTube videos, and it reads only those. It synthesizes, it cites the exact line it pulled from, and it won’t make things up, because it can only see what you handed it. Its whole power is faithfulness to your sources.

• The generative engine. This is Claude (Anthropic’s AI assistant, the main rival to ChatGPT) or Gemini (Google’s AI assistant). This engine reasons, argues, drafts, and creates. It roams everything it knows to build something new. That range is its power, and it’s also its risk, because an engine that can invent can also invent a fact you needed to be true.

Here’s the one difference to hold onto, because every decision downstream comes from it. A grounded engine is faithful. A generative engine is creative. Faithful means it stays inside the sources you handed it and never wanders, which is what you want when being wrong is expensive. Creative means it goes past your sources to build something new, which is what you want when the blank page is the problem. One is caged to your truth on purpose. The other roams free on purpose.

Grounding, by the way, just means tying the AI’s answers to specific documents you provide instead of letting it pull from the whole internet. That single idea is the hinge everything here turns on.

Quick Win (under 60 seconds) Take one thing you were about to ask Claude or ChatGPT to summarize from a YouTube video. Instead, drop the actual source into NotebookLM (it’s free) and ask it the same question. Watch it answer only from that document and show you the exact line it used. That gap, between a confident guess and a cited answer, is the entire point of the grounded engine.

When to reach for which

So the decision comes down to one question you ask before you start: is being faithful to specific sources the point, or is thinking and creating the point? Answer that and you’ve picked your engine.

Reach for the grounded engine when you’re working from real material and a made-up detail would cost you. That’s most of the high-stakes research a leader actually does:

• Comparing several years of reports to catch a trend nobody flagged

• Turning a stack of meeting transcripts into decisions and who-owns-what, anchored to what was actually said

• Reading a contract, a policy, or a financial statement and pulling out exactly what it says, with the citation

• Making sense of fifteen sources on a market before a board conversation

Reach for the generative engine when you’re starting from a blank page and the point is to think, argue, or build:

• Drafting the board narrative once you already know the facts

• Pressure-testing a strategy from three different angles

• Writing the memo, the email, the plan

• Brainstorming names, positioning, or options you haven’t thought of yet

Same leader, same afternoon, two different engines, because the jobs are different. You wouldn’t reach for a hammer to tighten a bolt, and you wouldn’t ask a tool built to invent to be your source of truth.

Here’s the 30-second version you can hand your team. Before any AI task, run down this:

• Am I working from specific sources I need it to respect? Grounded engine (NotebookLM).

• Would a made-up fact here embarrass me or cost money? Grounded engine.

• Am I starting from a blank page and need it to think or create? Generative engine (Claude or Gemini).

• Do I need it to argue, draft, or brainstorm? Generative engine.

• Do I need both? Ground first, then generate. That’s next.

My rule: ground, then generate

Most of the time, the honest answer to that last question is “both,” and the order is everything. Ground first, then generate.

Here’s where most of us go wrong. We take a messy pile of sources and dump it straight into a generative engine, then act surprised when it blends real facts with plausible filler. Flip the order. Put the sources into the grounded engine first, get one report you can trust, then hand that report up to the generative engine to reason and build on.

That’s exactly how I work. Most of my day lives in Claude, four businesses run through it. But when I’m researching something serious, I’ll pull seventeen or eighteen sources into NotebookLM (new to it? here’s a full hands-on walkthrough), including full YouTube videos, and have it build me one synthesized report where every line traces back to a source I chose. No invented statistics. No confident nonsense.

Then I hand that report to Claude, and now the generative engine does what it’s great at, strategy, drafting, turning that research into executive-level analysis, except it’s building on solid ground instead of guessing. The facts come from the grounded side. The thinking happens on the generative side.

The handoff itself is simple. Copy the report out of NotebookLM, paste it into Claude, and you’re done. If you want the cleaner version, you can connect the two directly so Claude reaches into your notebook on its own, but start with copy-paste. It works fine and it teaches you the rhythm.

The full playbook

This post is the decision. The full walkthrough of the grounded engine, the part your team will actually execute, is the field guide: The NotebookLM Leader’s Playbook, the do-it-as-you-read version with a screenshot of every step:

• Setting up your first notebook and the three-panel workflow

• Feeding it the mess: PDFs, living Google Docs, and YouTube videos, plus the one curation rule that decides quality

• Pulling grounded, cited answers and building one report you trust

• The handoff to Claude, both the copy-paste method and the direct-connection power move

• A 3-week plan to wire the two-engine stack into how your team works

You have two options to get the 37-page guide. One, click below to buy it for $5.99. Two, get it for free (plus all future ebooks and guides) by becoming a premium member for $49/year.

Get your E-book

You don’t need to rebuild your whole workflow tonight. You need one grounded rep in NotebookLM before you reach for Claude again.

If You Only Remember This

• AI splits into two engines: the grounded one (NotebookLM) that stays faithful to your sources, and the generative one (Claude or Gemini) that reasons and creates.

• The rule is ground, then generate. Pull your real sources into the grounded engine first, then hand that trustworthy output up to the generative engine to think and build on.

• Everybody’s using AI now. That’s not the edge anymore. Using the right engine for the job is.

Subscribe now

Worth Your Time

A few pieces from other creators I think are worth your attention.

• The AI Maker — Before You Try Another AI Tool, Ask These 7 Questions. A 7-question framework for deciding which tool earns a place in your workflow, tested head-to-head across Claude, Gemini, and NotebookLM, the “match the engine to the job” call in practice.

• Artificial Corner — NotebookLM: The Complete Guide. The clearest plain-English case for the grounded engine: answers anchored to the sources you upload, with examples like grant writing and customer research.

• Grow With AI — My Entire NotebookLM Prompt Library. 60 prompts built around source-grounding and synthesis, a working toolkit for the grounded engine.

Questions Leaders Are Asking

What’s the difference between NotebookLM and Claude? NotebookLM is a grounded engine: it only answers from the specific sources you give it, cites the exact line it used, and won’t invent facts. Claude is a generative engine: it reasons, drafts, and creates from everything it knows. Use NotebookLM when you need faithfulness to your documents, and Claude when you need thinking and creation.

Should I use NotebookLM or Claude for research? Use both, in order. Pull your sources into NotebookLM first so it can synthesize them into a report grounded in real material, then hand that report to Claude to reason, draft, or build on top of it. Grounding first is what keeps the final work from drifting into made-up details.

Is NotebookLM free? Yes. As of July 2026, NotebookLM has a free tier that covers uploading sources (up to 50 per notebook), asking grounded questions, and generating summaries. It’s the easiest no-cost way to try the grounded engine before you build any bigger workflow around it. Higher source limits and the newest features come with a paid Google AI plan.

Why does AI make things up, and how do I stop it? Generative engines like Claude or ChatGPT build answers from everything they know, so they can blend real facts with plausible-sounding filler. The fix is grounding: give the AI a fixed set of sources to work from, which is exactly what NotebookLM does, so every claim traces back to a document you chose.

Do I need to connect my tools to run this workflow? No. Start by copying the report out of NotebookLM and pasting it into Claude. That handoff works fine on its own. Connecting the two directly (through an integration called an MCP) is a convenience you can add later once the two-engine rhythm is second nature.

Can I use this with my whole team? Yes, and that’s the point. The two-engine decision, grounded versus generative, is exactly the thing everyone touching AI needs to get right, not just leadership. Send your team this post for the decision itself, and the NotebookLM Leader’s Playbook walks them through running the grounded engine step by step.

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

So here’s a question that’s showing up on more and more leadership desks right now: if there are so many free AI agents (software that takes action on your behalf, like reading an email and deciding where to route it) sitting on GitHub for free, why would you still pay someone to build one?

It’s a fair question. It’s also the exact question that cost one company three enterprise customers in 48 hours, which is where this week’s guest picks up the story.

I asked Srilakshmi Chiluvuri to walk us through it. She’s a full-stack professional with more than 15 years in IT and hands-on experience across AI and cloud, and she writes AI for Professionals, one of the sharper publications I’ve been reading on how these agent systems actually get built. You can subscribe to her here: AI for Professionals.

AI for Professionals

AI for Professionals

Her piece is about what happens after you download the free agent, the part nobody screenshots. Read it all the way through, and pay attention to the ALIGN framework near the end. I’ll get out of the way.

What to remember before you read

• Free agents can execute tasks, but they can’t weigh context, priority, or risk. That gap is the whole problem.

• The failure is almost always architecture, not the agent itself. A single misrouted escalation cost one company three enterprise customers in 48 hours.

• Four things fixed it: agent memory, guardrails, multi-agent routing, and human escalation when confidence drops.

• ALIGN is her deployment checklist: Assess repetitive work, Locate bottlenecks, Integrate context, Govern with guardrails, and measure outcomes (the acronym is hers).

• Before you deploy any agent, answer five questions. Start with the one most of us skip: what happens when the agent is wrong?

Here is Srilakshmi.

Why AI Agent Developers Are Still Winning—Even in a World Filled With 10,000 Open-Source Agents

Anyone can download an agent framework. Almost no one can make it survive contact with a real customer, a real edge case, and a real escalation.

In late 2025, a mid-sized logistics company in India believed it had found the shortcut every modern business dreams about.

Its leadership team had discovered hundreds of ready-made AI agents on GitHub — agents that promised everything from customer support to invoice processing and sales automation. The CTO’s question was direct: why hire AI agent developers when the internet already offers thousands for free? It sounded like a fair question. Until reality arrived.

Quick context before we get into it: this isn’t a story about bad agents. LangChain, Microsoft AutoGen, and the community orchestration tools the company used are genuinely capable frameworks. The gap wasn’t in the tooling — it was in everything wrapped around the tooling.

The experiment that saved nothing

The company downloaded multiple open-source agent frameworks — LangChain, Microsoft AutoGen, and community-built orchestration tools on GitHub. Within weeks, they had assembled what looked like a modern AI operations stack. On paper, it was impressive. The agents could read emails, extract invoice data, query databases, generate responses, and trigger workflows.

Then real customers entered the system, and the cracks appeared.

The real problem was never the agent

A client submitted a shipment escalation email. The AI agent read it, classified it incorrectly, and instead of routing the issue to operations, sent an automated billing response. Three enterprise customers escalated within 48 hours.

The company didn’t have an AI problem. It had an architecture problem. The open-source agents knew how to execute tasks. They did not understand business rules, customer escalation priorities, regulatory obligations, internal approval chains, or context switching across departments.

What the developer actually did

That’s when the company brought in an AI agent developer. Instead of building everything from scratch, the developer redesigned the system around business outcomes — connecting the existing agents to Slack for team escalation, Salesforce for customer history, SAP for supply chain data, and internal databases for decision memory. Then four critical capabilities were introduced.

1. Agent memory

The system remembered customer history instead of treating every request as a new conversation.

2. Guardrails

Controls were added to reduce hallucinations, misclassification, and prompt injection attacks.

3. Multi-agent routing

One agent classified intent. Another validated business rules. A third executed the required action.

4. Human escalation logic

Whenever confidence dropped below a defined threshold, the workflow was automatically handed to a human expert.

Result, 60 days later: response time dropped by 43%, operational errors fell by 61%, and customer retention improved. The business didn’t win because it found better agents. It won because it found better developers.

The leadership lesson hidden in the story

The real leadership question is not “how do we automate more tasks.” It’s: how do we build organizations where humans and intelligent systems create better outcomes together? Forward-looking organizations are discovering that AI does not replace leadership — it raises the standard for it. The leaders creating the greatest impact are focusing on four critical shifts.

1. From automation to intelligent decision systems

AI agents can automate tasks. Leaders must design systems that align automation with business priorities, customer trust, compliance requirements, and long-term strategy.

2. From managing teams to orchestrating human + AI collaboration

Tomorrow’s leaders will not only lead people. They will lead ecosystems that include employees, AI agents, data systems, and digital workflows working together.

3. From technical adoption to organizational transformation

The challenge is not implementing AI tools. The challenge is reshaping culture, processes, accountability, governance, and decision-making. Technology is often the easiest part of transformation — people and processes are where success is determined.

4. From efficiency metrics to strategic value creation

The most successful leaders will measure AI not by how many tasks it automates, but by how effectively it improves innovation, customer experience, employee productivity, and business resilience.

Turning insight into action: the ALIGN framework

Understanding why AI agent developers remain valuable is only the first step. The bigger challenge for leaders is translating these lessons into repeatable business practices. Organizations that succeed with AI are rarely the ones deploying the most agents — they’re the ones building the right systems, governance structures, and decision-making frameworks around those agents. A practical starting point is the ALIGN framework.

A Assess repetitive work

Identify tasks that consume significant time but require limited creativity, judgment, or strategic thinking. These are often the highest-impact opportunities for AI support.

L Locate decision bottlenecks

Find approval chains, information gaps, and repetitive reviews that slow down business operations. These bottlenecks often create the greatest productivity gains when improved.

I Integrate context sources

Ensure AI agents have access to relevant business context through CRM systems, databases, communication tools, knowledge bases, and operational platforms. Without context, even advanced AI systems make poor decisions.

G Govern with guardrails

Define security policies, compliance requirements, escalation paths, and human oversight before deployment. Good governance prevents expensive mistakes later.

N Measure business outcomes

Track metrics that matter: customer satisfaction, error reduction, revenue impact, retention, productivity improvements, operational efficiency. If outcomes aren’t improving, automation alone isn’t creating value.

The 5-step AI leadership checklist

Before deploying any AI agent, leaders should be able to answer five questions.

1. What specific business problem are we solving?

2. Which decisions should remain human-controlled?

3. What systems and data sources must the agent access?

4. What happens when the agent makes a mistake or lacks confidence?

5. How will success be measured after 30, 60, and 90 days?

Organizations that can answer these questions clearly are far more likely to achieve measurable results than those simply experimenting with the latest AI tools.

The future belongs to leaders, not just technology

The future will not be led by companies with the largest collection of AI agents. It will be led by organizations that combine intelligent systems, skilled developers, disciplined governance, and strong leadership. In 2026, competitive advantage will come from knowing not only what AI can do, but how to deploy it responsibly, strategically, and at scale.

Open-source agents will continue to multiply. AI models will continue to improve. But the organizations that create lasting value will be the ones that understand a simple truth: technology may power the system, but leadership determines the outcome.

P.S. If you’re evaluating whether to build in-house or hire an AI agent developer, start with question four from the checklist above: what happens when the agent is wrong? If you don’t have an answer yet, that’s your first project.

Which part of the ALIGN framework is hardest for your organization right now — assessing the work, locating bottlenecks, integrating context, governance, or measurement? Reply and tell us, and we’ll dig into it in a future issue.

Thank you, Srilakshmi!

If you’re weighing build-versus-buy on your own team, start where she ends: what happens when the agent makes the wrong call?

I’m going to keep learning this one alongside you. For more, subscribe to Srilakshmi’s AI for Professionals.

And if you want help drawing those lines for your own team, what to automate, what stays human, and what to guard, that's what we build in the AI Judgment Workshop. Ninety minutes live, $99 (50% off for premium members).

Which part of ALIGN is hardest for your team right now?

Questions Leaders Are Asking

What is an open-source AI agent? An open-source AI agent is free software that takes action on your behalf, like reading an email, pulling data, or triggering a workflow, using a framework anyone can download from a site like GitHub. The agent can execute those tasks, but on its own it has no memory of your business, no guardrails, and no sense of priority.

Why hire an AI agent developer if the agents are free? The framework is the easy 20%. A developer builds the other 80%: connecting the agent to your real systems, adding memory and guardrails, routing work between multiple agents, and handing off to a human when confidence drops. That wrapping is what makes an agent safe to put in front of a customer.

What is the ALIGN framework for deploying AI agents? ALIGN is a five-step deployment checklist from Srilakshmi Chiluvuri: Assess repetitive work, Locate decision bottlenecks, Integrate context sources, Govern with guardrails, and measure business outcomes. It shifts the focus from how many agents you deploy to the systems and governance you build around them.

What are AI agent guardrails? Guardrails are the controls that keep an agent inside safe limits. They reduce hallucinations (confident but false answers), catch misclassification, and block prompt injection, where hidden instructions try to hijack the agent. Good governance defines these limits, plus escalation paths and human oversight, before deployment, not after something breaks.

When should an AI agent escalate to a human? Whenever the agent’s confidence drops below a defined threshold, or the decision carries real risk, cost, or customer impact. In the case study, routing low-confidence work to a human expert cut operational errors by 61% over 60 days. The rule of thumb: high-stakes and low-confidence decisions stay human-controlled.

Should my company build AI agents in-house or hire a developer? Start with one question: what happens when the agent is wrong? If you can’t answer that, you’re not ready to ship, whoever builds it. Free frameworks lower the cost of the agent itself, but the memory, guardrails, routing, and escalation logic around it are where the real work and the real risk live.

Srilakshmi Chiluvuri is a full-stack professional with more than 15 years in IT and expertise across AI and cloud. Srilakshmi writes AI for Professionals on Substack, covering how to build agentic AI systems that hold up in the real world.

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

So here’s something I preach to every leader I work with. To do work that matters right now, you have to answer two questions. First, what will you never let AI touch? That’s your craft. It’s where your expertise lives, the part you have to finish yourself, because the moment you take yourself out of it, the work stops being yours. Second, what keeps getting in the way of that craft? The busywork, the time-wasters, the stuff you dread. That second list is exactly what AI is for. I’ve started calling that dividing line the craft line: everything above it is yours to keep sharpening, everything below it is a candidate for AI.

For me, that line runs right through my interviews. I love doing them. Sitting with someone like and actually connecting with the person in front of me, that’s the part I’ll never hand off. What I never loved was everything after: transcribing the recording, adding timestamps so Apple Podcasts and Spotify could keep up, rendering the graphics.

I love the interview. I dreaded the assembly line behind it.

So I automated the assembly line, not the interview. And here’s the part most people get wrong before they even start, they assume Claude has to watch the video to do this. It doesn’t. I checked Anthropic’s own documentation, and it’s plain, Claude reads text and still images and that’s it. No video, no audio, not even a moving GIF. You don’t need it to watch anything, because everything worth pulling out of a conversation is already in the transcript, and the transcript is what Claude is genuinely good at.

Let me show you the exact workflow, using the real Polina episode. About twenty minutes, five publishable assets from one recording.

In this post, you’ll learn:

• How to get a clean, timestamped transcript from any recording

• The prompt that turns that transcript into chapter markers for Apple Podcasts, Spotify, and YouTube

• How to pull the key themes and the two or three quotes worth making into graphics

• Where the whole thing breaks if you’re not careful, and the one line that fixes it

Step 1: Get the transcript, because that’s the raw material

Everything downstream depends on one file: a transcript with timestamps. Not a summary, not your memory of the conversation, the actual words with the times attached.

If it’s a Substack Live, a podcast, or a YouTube video, you already have auto-captions you can export. If it’s a recorded webinar, a panel, or a leadership town hall you ran, the same thing applies. For the calls and meetings I want to keep, I run Granola, because it sits in the background and captures the whole thing without turning the meeting into a recording session. However you get there, the goal is the same: a text file with words and timestamps.

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On Substack, simply click Edit, click on Transcript, and Download (ideally json).

That file is the whole ballgame. Think of the recording like a cut of meat. Most of us use a tenth of it and throw the rest out. The transcript is how you get five meals off one roast.

Quick Win (< 60 seconds) When you paste your transcript in, add this one line: “Use the exact start-time from each transcript segment, formatted MM:SS. Do not estimate or round.” That single instruction is the difference between timestamps your listeners can trust and timestamps that send them to the wrong spot.

Step 2: Turn the transcript into chapter markers

This is the piece almost nobody does by hand, because doing it by hand is miserable. You paste the transcript into Claude and ask for it directly:

Copy-Paste Prompt: Here is the timestamped transcript of an interview. Identify the 15 to 20 key moments in the conversation. For each one, give me the timestamp in MM:SS using the exact start-time from the transcript, and a short description of what’s being discussed at that point. Format it as a clean list I can paste into YouTube, Apple Podcasts, and Spotify as chapters.

Here’s what Claude gave me back for the Polina conversation, and I pasted it straight into the episode:

• (01:13) A cold read: what ten minutes reveals

• (03:44) The mask, access, and Logan Paul’s lollipop

• (04:45) Kevin O’Leary and the villain he plays

• (11:21) Scaramucci’s Superman, everyone’s Batman

• (19:48) Long-form in a short-form, AI-flooded world

• (22:02) “When I write, I think”

And on Spotify, Youtube, and Apple Podcasts, each time stamp will take the listener to the exact spot on the podcast.

Those aren’t decoration. Chapters are what make a long recording findable, which is what makes people actually watch it. And they took me one prompt instead of forty minutes of scrubbing a timeline.

The first time this ran start to finish, the interview stopped having a tax attached to it. I could sit in the conversation and enjoy it, without the cleanup work hanging over the rest of my night.

Step 3: Pull the themes and the quotes worth keeping

A recording isn’t just moments, it’s arguments. The same transcript that gives you chapters will give you the two or three ideas the whole conversation was really about, if you ask for them.

Copy-Paste Prompt: From the same transcript, pull the 3 to 4 biggest themes of the conversation. For each theme, give me a one-line headline and the single strongest quote from the guest that captures it, word for word, under 30 words.

For the Polina episode that surfaced the spine of the whole thing: we are how we move through the world, villains get to be human in a way heroes don’t, and the one line I keep going over, “when I write, I think.” That last one is the reason she won’t hand her writing to AI even though she uses it every day, and it’s the same discipline behind the AI Leadership Triad. The tool sharpens your judgment. It doesn’t get to replace it.

Then you take the strongest standalone quote and make it a graphic. Claude picked “Villains are allowed to be human,” and that became the quote card for the post. One line, pulled from the transcript, formatted, and out the door.

There always are a few paragraphs of context that I add before each transcript prompt is run adding what I got out of the interview, key pieces to highlight, context, my own stories, etc. Make sure that your voice, your life, your experience, is in our content. That’s what makes it yours.

Step 4: The render, so you have something to look at

The last asset is the one people see first: the thumbnail and the quote card. Claude doesn’t draw these, but it writes the brief so well that generating them is fast. Ask it for a thumbnail concept and a headline of four words or less, hand that to your image tool, and you’re done.

For Polina, the recording gave us the whole set: the “How the Best Minds Think” thumbnail with her and the Hidden Genius cover, the “Villains are allowed to be human” quote card, chapters, themes. Five publishable assets from one conversation, in the time it takes to drink a coffee.

NOTE: By the way, Claude found the book cover, the profile image, rendered it, and gave the final copy. In order to get to one you like, your brand ALREADY needs to be in Claude as a skill, AND it will take several iterations to get to your style. SAVE that as your new skill.

If you want help resetting your own rollout, that's what we build in the AI Judgment Workshop. Ninety minutes live, $99, and you leave with a written 90-day plan instead of another framework to file away.

Doing this for every recording, in your own brand

The workflow above works today, in the normal Claude app, with no setup. But if you record often, you don’t want to paste four prompts every time. You want it to run in your voice, your colors, your show name, once.

That’s a Claude skill: a saved setup that already knows your brand, so you hand it a transcript and it hands you branded chapters, themes, quotes, and a render brief. I built mine so I never re-explain Leadership in Change again. Mine runs in Claude Code (Anthropic’s coding-focused version of Claude), because that’s where I spend my time, but the same skill works in the regular Claude app too. If Claude Code is new to you, start with The Non-Coder’s Guide to Claude Code, and I wrote about how skills like this save me forty-plus hours a month in 5 Claude Skills That Save Me 40 Hours Monthly.

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Your Shortcut (Premium Members)

Instead of building the skill from scratch, I made a single prompt that interviews you about your brand and writes the skill for you. Answer its questions once, and it outputs a skill that turns any future transcript into assets that look like yours.

The Brand Skill Builder Master Prompt

This next part is for premium members. The Brand Skill Builder prompt below interviews you about your brand, then writes a custom Claude skill that turns every recording you make into chapters, themes, quotes, and a render brief in your own voice and colors. Premium members get the full prompt right here. If you’re on the free list, upgrade to premium to unlock it, along with the rest of the member library.

Subscribe now

If You Only Remember This

• Draw the craft line first: name the one thing you’ll never let AI touch, then hand it everything standing in the way of that thing.

• Claude can’t watch your video, but the transcript holds almost everything you wanted from it anyway.

• Doing this once saves you an evening. Saving it as a skill saves you every evening after that.

Worth Your Time

A few pieces from other creators I think are worth your attention.

• Grow With AI — LinkedIn OS. A reusable Claude Skill that turns creator research into ready-to-post content ideas, the same “build the skill once, reuse it forever” move behind turning one recording into many assets.

• Prosper in AI — The SEO Loop I Run on Every Post. A repeatable Claude workflow that reads your draft plus a folder of reference files and runs automated passes before you hit publish, the busywork automated, the craft kept.

• Claudia + AI — Claude Skills Starter Guide. A no-code, plain-English walkthrough of turning a repeatable process into a reusable Claude Skill, the exact “save your workflow as a skill” step, written for creators, not engineers.

Questions Leaders Are Asking

Can Claude watch or analyze a video? Not directly. Claude reads text and still images, not video or audio. To work with a recording, you give Claude the transcript, and optionally a few screenshots of key frames. That transcript is where the chapters, themes, and quotes come from.

How do I make podcast chapter markers with AI? Paste your timestamped transcript into Claude and ask for 15 to 20 key moments, each with the exact start-time in MM:SS and a short description. Tell it not to estimate the times. Then paste the list into YouTube, Apple Podcasts, or Spotify.

Do the timestamps come out accurate? Only if you make Claude use the transcript’s real start-times. Left on its own, it will sometimes estimate and drift. Add one line telling it to use the exact segment start-time and never round, and the chapters line up.

Can I make this run in my own brand automatically? Yes. Save the workflow as a Claude skill that already knows your show name, colors, and platforms. Then you hand it a transcript and it returns branded assets. A skill is a saved setup Claude reuses, so you never re-explain your brand again.

Do I need Claude Code or any technical setup for the basic workflow? No. The core workflow runs in the normal Claude app by pasting your transcript and the prompts above. The only thing that needs a bit more setup is turning it into a reusable branded skill.

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

Affiliate links included.

What to Remember

• The one-sentence test Nir uses to tell traction from distraction, which has nothing to do with what the activity actually is

• Why he calls the to-do list a broken tool, and the timeboxing move he replaced it with

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🎧 Prefer audio? Listen on Apple Podcasts

Outline

(1:19) – Hooked and Indistractable are complements, not opposites

(5:07) – Internal triggers, and the month I deleted every news app

(6:24) – The book about focus Nir can’t stand

(7:22) – Only 10% of distraction comes from the pings

(8:56) – Traction, distraction, and the Latin root of both

(10:49) – The email trap: work that isn’t the work

(12:27) – Name one product built to help you use it less

(14:20) – Effort pacts, and Viktor Frankl’s space

(15:43) – Why to-do lists are broken

(17:22) – Timeboxing, and the real metric of success

(22:30) – Self-regulation is the skill of the century

(28:36) – Persistence, belief, and Beyond Belief

My Takeaways

Ninety percent of it is coming from inside the house

So this is the number I had to go back and check twice. We blame the pings, the dings, and the rings, and Nir puts those at roughly 10% of the problem. The other 90% is an internal trigger, which is really just a feeling:

“Loneliness, boredom, fatigue, uncertainty, anxiety.”

Then he closes the escape hatch.

“It’s not your fault. You didn’t create these digital technologies. But it is your responsibility because who else is responsible?”

Traction and distraction are the same word

Here’s the reframe I’ll actually use. Both come from the Latin root trahere, to pull, and both end in the same six letters, action. So the test is simple:

“anything that is not what you said you were going to do in advance is by definition a distraction.”

Watching football isn’t a distraction if you planned it, and answering email is one if you didn’t. That second half is what got me, because Nir says the work-shaped distractions are the worst kind, “because you don’t even realize you’re distracted.” I said on the recording that this was hitting close to home, and I meant it.

The to-do list is a register of things you failed to do

Honestly, this is the part I didn’t want to hear because I tend to live by my to do list. A to-do list has no constraints, so you always add more and you always pick the easy item over the important one, and then you read back the leftovers night after night until you’ve built a fairly convincing case against yourself.

“There’s nothing wrong with you. You’re not broken. It’s this stupid time management technique we call to-do list that’s broken.”

His replacement is timeboxing, and it moves the finish line: success isn’t the checked box but “did I work on what I said I was going to work on for as long as I said I would without distractions?”

Be stingy with your time and generous with your money

This is the line I’d put on a wall. We clip coupons, we split checks at lunch, we hunt for deals, and then we hand our hours to whoever asks. “You can’t make more time. I don’t care if you’re Bill Gates or Elon Musk.” He carries it into parenting, calls self-regulation “the skill of the century,” and describes sitting down with his six-year-old to build her calendar, video games included. My version is a Bloom card that locks my work apps at 8 p.m., so getting back in means walking to my bedroom and tapping my phone against it. That walk is the whole point.

One Question to Sit With

Then watch the full conversation above, and go subscribe to Nir And Far, grab Indistractable, and pick up his new one, Beyond Belief.

About Nir Eyal

Nir Eyal is the author of Hooked, Indistractable, and Beyond Belief, which are built on his work in behavioral design and human motivation. Hooked and Indistractable have sold more than a million copies in over 30 languages, and Beyond Belief is an instant New York Times bestseller. He’s a former lecturer at the Stanford Graduate School of Business and the Hasso Plattner Institute of Design at Stanford, and his writing has appeared in The New York Times, Harvard Business Review, and The Atlantic. He writes Nir And Far on Substack and gives away a deep library of free tools at nirandfar.com, including a schedule maker and a habit tracker.

Nir And Far by Nir Eyal

The clearest thinking on what drives human behavior

About me

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

So, when an AI rollout stalls, what actually stalled?

Most of us reach for the tool answer first. The model, the licences, the integration, the training that nobody showed up to. I’ve come to think that’s the easy thing to look at, because the harder thing is the people, and the people are where these projects really live or die.

The way I think about AI is the way I think about fire or electricity. World-changing to have around, but just having it doesn’t make anything better on its own. You have to be deliberate about how your people actually pick it up and use it.

That’s exactly the ground works on. Brennan spent a career in financial services technology and change, and now he writes Getting AI To Work, where the whole premise is that AI initiatives stall because of people, not technology.

Getting AI To Work by Brennan McDonald

I help leaders get AI initiatives unstuck.

What to remember from this piece

• When an AI project stalls, look at the people and the operating model before you look at the tool.

• Run three questions before you roll anything out: what are we making better, what will change for our team, and how will we do this properly.

• “Making better” is about time, judgment, and creativity, not just cost. Ask the people who do the work if they agree it’s a problem worth solving.

Here’s Brennan.

Reframe Your AI Change Initiative

There are clear benefits to deploying AI for leaders. You can do things more efficiently, reduce costs, or increase outputs. What I’ve found over the course of my career, and in my AI experimentation and work, is that the focus is too often on the tools. We fall in love with the solution and forget about the people required to get it over the line.

The idea of a vision and the concept of putting people first are sometimes dismissed as corporate cliches. Yet the lesson of many AI projects so far is that successful projects have great change management, cultural buy-in, and leaders who have thought carefully about how to bring their people on the journey.

In today’s article, I’m going to share three questions to reflect on when you are setting up your AI initiative, especially if you are struggling to get that cultural shift and need a reset.

1. What are we making better?

2. What will change for our team?

3. How will we do this properly?

If you run through these questions, you’ll surface some of the blockers in your business, but also the boosters that will help you land important AI projects. We are in the age of technology, but people matter more than ever.

What are we making better?

Meaningful work matters more than it ever has before. What leaders want to think about in AI transformation is: what are we making better? This isn’t just about cost. We want to understand where people are wasting time, losing the chance to apply expert judgment, wasting energy, or not being able to exercise creativity today.

AI is an opportunity to make things better by automating repetitive tasks and simpler decision-making processes, freeing people up to focus on putting their best energy into the most important problems that your business faces.

What are we really trying to improve? By implementing these AI changes, what is better? Do the people who do the work agree that this is a problem to be solved?

A good answer here sounds like faster decision-making cycles or shorter feedback loops, a reduction in admin or manual tasks, improved customer service standards and responsiveness, or safer operational flows with lower levels of risk.

What will change for our team?

One of the biggest reasons for project failure of any kind is people not buying into the future state. They do not feel that they benefit from the change that is happening.

We want to ask questions like: Who gets spare capacity or the opportunity to work on different problems if this works? Who loses control of gateways or status in the workplace? Are there ways of working that have been in place that people feel invested in, or that they feel their ongoing job security is linked to?

If we roll this change out, what happens to that, and to people’s comfort with how things are done? Are we being open and honest about exactly why we’re making this change? Can people see through the messaging?

In my Substack and in my videos, I speak a lot about how what people see in their social media feeds interacts with what they hear in the workplace. Leaders need to be open and honest about what the change genuinely means for career growth, job security, pay and benefits, and other workplace issues people care about.

This matters because you are operating in an information environment that tells many people the only change that is going to happen to them because of AI is that they lose their job.

As part of your AI initiative, you can do things better, faster, and cheaper. You can help people solve more interesting problems. You can help them become more technical so that they can get leverage out of their unique skills, experience, and domain knowledge in using AI tools to solve problems.

How will we do this properly?

This is the execution question. This is where a lot of the thinking about governance or risk management comes into play.

All these topics are important. Too often, companies fall into a governance theatre arrangement. They have all the policies, frameworks, and security controls. They miss how they make AI adoption safe, useful, and real.

When I think about how to do AI transformation properly, I mean taking advantage of the technology. I don’t want to be trapped by the limitations of an existing operating model.

That comes down to whether you are redesigning workflows end to end, or just adding AI as another step in a process and putting it into a standard operating procedure. Can your people challenge, override, or question which AI models are used and how they are used? What guardrails are in place? Where is it okay to use AI-generated content, and where is it not? What thresholds require a human in the loop?

One thing I observe and hear in the conversations I have is that firms that have copied and pasted their approach to governance and risk management, without tailoring it to their own business, often run into problems that make it harder to realise the project benefits.

When you think about how you do this properly, this is like letter-of-the-law compliance versus spirit-of-the-law thinking. If you put AI into a workflow to be able to tell your stakeholders that you are doing AI, you absolutely are not doing AI properly.

If you take a step back and think about your operating model, your people, your processes, and your platforms, and think carefully about the bare minimum you actually need if you push this technology to its logical extent over the next few years, that is what I mean with this question.

Are you doing AI properly? Very few companies are. Even some of the biggest companies in the world are still making incremental changes to how they do things. They are risk averse. There is a lot of cost, complexity, and skill required to execute complex change of any type, let alone change involving an emerging technology that brings a whole set of other risks that must be managed alongside its deployment.

And that’s why taking a step back and getting clarity upfront about what you’re making better, what will actually change for your people, and how you are going to do AI properly is worth the time. Rushing licences to people with no operating model change is a recipe for lukewarm adoption and internal pushback. When it’s easier than ever to get AI to help generate a project plan, spending more time at the whiteboard thinking about how to make the work better has more leverage than ever.

Thank you, Brennan!

Brennan’s three questions do one thing well: they move the conversation off the tool and back onto the people who have to live with it. That’s really a test of adaptability, which is the first skill in the AI Leadership Triad I write about here, because the reset only works if you’re willing to change how the work actually happens, not just what software sits on top of it.

Rushing licences to people with no change to how the work happens is just a more expensive way to stall.

If this resonated, subscribe to Brennan’s Getting AI To Work. He goes deep on exactly this kind of reset every week.

If you want help resetting your own rollout, that's what we build in the AI Judgment Workshop. Ninety minutes live, $99, and you leave with a written 90-day plan instead of another framework to file away.

Questions Leaders Are Asking

Why do most AI initiatives stall? Most AI initiatives stall because of people and operating models, not the technology itself. Teams don’t buy into the future state, no one is honest about what changes for their jobs, and governance gets copied instead of designed. The tool usually works. The rollout around it doesn’t.

How do I get my team to actually adopt AI? Start by naming what the change means for them: job security, career growth, pay, and status. People adopt AI when they can see a real benefit for themselves, not just for the company’s cost line. Ask the people who do the work whether the problem you’re solving is even a problem worth solving.

What is governance theatre in AI adoption? Governance theatre is having all the policies, frameworks, and security controls in place while missing the actual point: making AI adoption safe, useful, and real. It usually happens when a company copies another organization’s governance approach instead of tailoring one to its own operating model and risks.

What questions should I ask before starting an AI project? Ask three before you roll anything out. What are we making better, and do the people doing the work agree it’s a problem? What will change for our team, honestly? And how will we do this properly, meaning are we redesigning the workflow or just bolting AI onto the old one to say we did.

How is AI change management different from regular change management? The fundamentals are the same: vision, buy-in, honesty. What’s different is the information environment. Many people already believe AI’s only effect on them is losing their job, so leaders have to be more direct than usual about what the change actually means for careers, pay, and day-to-day work.

Should I redesign workflows or just add AI to a step? Redesign the workflow. Bolting AI onto an existing standard operating procedure so you can tell stakeholders you’re “doing AI” is the fastest route to lukewarm adoption. The payoff comes from rethinking the operating model around what the technology can actually do, not from adding one more step.

Joel Salinas is an AI Strategy Coach for founders and leaders, from solopreneurs to teams. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Brennan McDonald writes Getting AI To Work, where he helps leaders get stalled AI initiatives unstuck after a career in financial services technology and change. Find him on YouTube and LinkedIn.

Written by a human, for humans.

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Outline

(00:25) – Who is, and why it’s never about the technology

(01:39) – From the newsroom to Forrester to Altimeter Group

(03:41) – Electricity, steam-powered factories, and why AI moves faster

(06:24) – The biggest misconception, and “you’re not even close to beginning”

(08:44) – Fluency vs. literacy, and why the only way through is through

(11:36) – The trust wall, and how a leader destigmatizes AI in one meeting

(13:48) – Why write a book on AI when everything keeps changing

(16:42) – Step one, and the one line to add to the end of every prompt

(19:27) – Pick your lane, and the board member who owned ethical AI

(25:35) – “You’re not behind. You’re just getting started.”

(33:09) – Curiosity, imagination, and using AI to find your blind spots

My Takeaways

You’re not behind. So this is the piece I keep coming back to, because I hear that fear from almost every leader I sit with and I’ve felt it in myself. Charlene has been in this since she helped a newspaper go online in 1993, she wrote the book on it, and she still took three weeks off in January to care for loved ones, came back to Claude Cowork, and had no idea what had happened while she was gone. She felt behind too. Then she took a breath and turned it around on the rest of us.

“You’re not behind. Just look around you. How many people haven’t even started yet? So if you start today and just do one thing, you’ll be ahead of the vast majority of people.”

One of my clients is a CEO with over 45 years of practice who started sending me links to artifacts he’d built in Claude, and he could not believe he made them himself with nobody helping him. That’s what starting looks like.

Fluency is a practice, and practice is the only door. Honestly, this is where most AI advice falls apart, because it tells you what the thing is and then leaves you standing there. Charlene splits AI literacy, knowing what it is, from AI fluency, which is what happens once you’ve used it enough that you stop thinking about it. There’s one way to get there and she doesn’t pretend otherwise.

“It will feel messy. It will feel like you don’t know what you’re doing... But there is no way around it. You have to go through it. The only way through it is through it.”

I said something close to this on the live about electricity, that plugging AI into an organization nobody wired for it does nothing, but hers is the harder version, because wiring a house is a project you finish and this is a thing you only get by being bad at it for a while. She also gave the best beginner tip of the night: add “before you start, ask me any clarifying questions you may have” to the end of your prompt, and the whole thing turns from a vending machine into a conversation.

Mindset comes before tools, and before governance. Look, most of us hand AI to the CIO, buy everyone a license, and call that a rollout, and I keep watching it play out the same way. Her line on it was the sharpest thing she said all evening.

“This is not something you delegate. And what I’ve seen now is that leaders are abdicating this role.”

The fix she gave is almost embarrassingly small. Use AI to prep for your next meeting, tell the room you did it, then ask them how they used it, which sets the expectation and takes the shame out of it in the same breath. If your people think using AI counts as cheating, no tool budget on earth saves the rollout. That’s the same wall I wrote about in the empathy gap in AI implementation.

The thing I’m stealing from her. Here’s the part I hadn’t thought of, and I said so on air. Charlene feeds her own meeting recordings to AI and asks it what went wrong in the room.

“Take a transcript of my last meeting because something in it didn’t feel right. Something went off the rails and I’m not sure what’s happening. Diagnose that and give me suggestions.”

It comes back and tells her she never set an agenda, or that she talked for 70% of a call she should have been listening through. I’ve fed transcripts into Claude plenty of times to build a post out of them, and it never once occurred to me to point that back at myself and ask where I got in my own way. I use Granola to capture the calls, so the raw material has been sitting there the whole time, which is really the argument for why none of us is behind. The thing that moves you forward this week is probably already open on your screen, and you just haven’t pointed it at anything that matters yet.

One Question to Sit With

Charlene left this one hanging and I haven’t been able to put it down:

Watch the full conversation above for her answer, and for the line I keep repeating to myself, that you’ve never been in control anyway. When you’re done, go subscribe to Charlene Li on Substack.

About Charlene Li

is a New York Times bestselling author whose books include Groundswell, Open Leadership, and The Disruption Mindset. Her latest, Winning with AI: The 90-Day Blueprint for Success, is co-written with Dr. Katia Walsh, one of the world’s first chief AI officers, and it’s a leadership and strategy book rather than a technology book. She founded Altimeter Group in 2008, spent years as a Principal Analyst at Forrester Research covering the rise of social media, served as Chief Research Officer at PA Consulting, and now leads her own firm, Quantum Networks Group. She’s just moved her newsletter to Substack, and the book is at winningwithaibook.com.

About me

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through a monthly live workshop and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.

Affiliate links included.

Written by a human, for humans.

TL;DR: Answer Engine Optimization (AEO) is the practice of getting your business cited and recommended inside AI answers from tools like ChatGPT, Google AI Overviews, and Perplexity. Traditional search optimization earns rankings; AEO earns the recommendation. This 2026 guide explains, in plain English, how AI decides who to name and how leaders can become the answer.

I got on a call with a new coaching client a few weeks ago, and one of his answers shocked me.

I asked him the question I ask everyone: how did you find me? Almost every client I’ve ever worked with came in through my newsletter. They read something, they reply, we end up talking. So I expected the usual answer.

He told me he’d never heard of my newsletter.

What he’d actually done was describe his problem to Claude. He asked it who could help him. And Claude gave him my name.

A real client, real money, a real coaching relationship, and the entire introduction happened inside an AI conversation I never saw, never optimized for, and didn’t even know was running. He didn’t Google me, he didn’t stumble onto a blog post, an AI vouched for me and he trusted it enough to reach out.

That’s the shift I want to walk you through, because it’s already happening to your business whether you’re watching for it or not.

It’s called Answer Engine Optimization, or AEO, and it’s the difference between being a link someone has to find and being the name an AI hands them.

In this guide, I’ll walk you through:

• What AEO actually is, in plain English, and how it differs from the SEO you already know

• Why AI is already forming opinions about your brand, and how to see what it’s saying

• The five honest moves that make AI more likely to recommend you

• A simple three-prompt test you can run on your own company in the next ten minutes

What Is AEO, in Plain English?

Answer Engine Optimization (AEO) is the work of getting your business named inside the answers AI tools give, instead of just ranking on a results page. When someone asks ChatGPT, Google’s AI Overview (the AI summary that now sits above the blue links), or Perplexity (an AI-powered search engine) for a recommendation, AEO is what decides whether your name comes up.

Here’s the way I think about it. Old-school search is the phone book. SEO, search engine optimization, the work of ranking well on Google, got your business listed in that book, hopefully near the front. But nobody asks the phone book who to trust. They ask a friend. And when a friend who knows the field says “oh, you should call this person,” that one recommendation carries more weight than ten pages of listings.

For a lot of people, AI has become that friend. They don’t scroll ten blue links anymore. They ask Claude or ChatGPT a real question and take the answer it gives. So the prize stopped being the click and became the recommendation. SEO gets you into the room. AEO gets you recommended once you’re there.

You’ll also see this called GEO, generative engine optimization. People use the two terms almost interchangeably. Same idea: be the thing the AI quotes.

And to clear up the question I get immediately: no, you don’t abandon SEO. SEO is still the foundation, and it always will be. A lot of what AI pulls from is built on solid search presence. AEO is the new layer you add on top, and the reason to add it now is simple. The window is open. Most of your competitors haven’t started.

Quick Win (< 60 seconds): Open ChatGPT or Claude right now and type: “What does [your company] do, and who is it for?” Read what comes back. Whatever it says, accurate, outdated, or nothing at all, that’s your starting line.

Seriously, stop reading for a second and do it. I’ll wait.

AI Is Already Talking About Your Brand

Whether or not you’ve thought about AEO, AI tools are already describing your business, comparing it to competitors, and sometimes criticizing it. The catch is you can’t see those conversations, and they aren’t always accurate or kind.

The software company BrightEdge ran an analysis in 2026 of how the major AI engines talk about brands, and the numbers should get every leader’s attention. Google’s AI Overviews are 44% more likely than ChatGPT to surface negative sentiment about a brand. And ChatGPT is 13 times more likely than Google to criticize a brand right at the moment someone’s deciding whether to buy. Same company, two AI engines, completely different verdicts. The two of them disagree on which brand to criticize 73% of the time.

When I read that, I had to read it twice. It means there isn’t one answer about your business out there. There are several, they don’t agree with each other, and you’re not in the room for any of them.

This is happening because of a bigger change underneath everything. More and more searches now end without a single click. The answer shows up on screen, the person reads it, and they never visit anyone’s website. Researchers call this zero-click. A European Parliament study in 2025 found that visits from search to news sites dropped from 2.3 billion to 1.7 billion in a single year, while the share of news searches ending in zero clicks climbed from 56% to nearly 69%. Business Insider alone lost about 55% of its search traffic.

I’m not telling you this to scare you, and I’m not interested in doom. I’m telling you because the attention you used to count on is moving into a place you can’t see, and the businesses that adapt to that are the ones AI keeps recommending. The ones that ignore it slowly fall out of the conversation. I first wrote about this over a year ago in How to Be Seen in the AI Search Era. Everything I worried about then has only sped up, and the data since has made the case stronger.

Why an AI Recommendation Beats a Click

Leaders hear “fewer clicks” and panic. But the visitors who do arrive through an AI recommendation are usually further along and readier to buy, because the AI already did the research, the comparison, and the vetting before they ever reached you.

Think about how my client actually got to me. He didn’t browse. He described his problem, the AI weighed the options, and by the time he reached out he’d already decided I was the right fit. Weeks of research and trust-building happened inside a conversation that took him a few minutes. He showed up pre-sold.

That’s the pattern. Data from the analytics firm Conductor suggests visitors who arrive from AI tools convert at roughly four times the rate of regular search visitors, and they spend longer on the page once they land. I’d take one of those over ten cold clicks any day of the week.

We’re getting close to a world where the AI isn’t just answering the question, it’s doing the shopping. I wrote about that in How AI Agents Are Replacing Online Shopping. If the AI is the buyer’s first stop, then the AI is the audience you have to win.

And here’s the part that reframes the whole job. When AI decides who to recommend, it mostly isn’t quoting your website. Studies of AI citations in 2026 show that somewhere between 90 and 95% of what AI pulls from comes from third-party sources: other people’s articles, forums, reviews, roundups. Only 5 to 10% comes from your own site. So the conversation about your business is mostly happening on other people’s pages, and a big part of the job is influencing those.

This guide gives you the map.

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How to Become the Answer: 5 Moves

Becoming the brand AI recommends comes down to five moves: audit what AI already says about you, answer questions directly on your own site, earn mentions on the third-party pages AI trusts, keep your facts consistent everywhere, and check back regularly.

1. Audit what AI already says about you. Before you change anything, find out what the AI thinks. Open ChatGPT, Gemini (Google’s AI assistant), and Perplexity in a private or logged-out window so your own history doesn’t bias the result, and ask about your company and your category. I’ll give you the exact prompts in a minute.

2. Answer questions directly, on your own pages. AI likes content that answers a specific question fast. The research backs this up hard. A Princeton study on AI visibility found that adding clear statistics to your content makes AI about 30% more likely to cite it, citing your own sources adds another 30%, and quoting a credible expert adds 41%. Keyword-stuffing, the old SEO trick, actually makes you 9% less likely to be cited. So write like a helpful human. Put the answer first, then explain.

3. Earn mentions where AI looks. Remember, around 90% of what AI quotes lives on other people’s pages. That means the roundups, the “best tools for X” lists, the Reddit threads, the YouTube videos. If you’re not in the comparison article AI keeps citing, you either get into it or you create a better one.

4. Keep your facts identical everywhere. AI is, at its core, a consensus machine. When your company’s description, services, and details match across your website, your LinkedIn, your Google profile, and review sites, the AI trusts the facts and repeats them. When they conflict, it often just leaves you out rather than risk being wrong.

5. Check back, because the answer changes. AI answers aren’t fixed. They shift as new sources show up. Re-run your audit every couple of months, watching especially for wrong claims and the near-purchase criticism we talked about earlier. Treating AEO as a one-and-done box to check is the same trap behind why most AI investments fail.

One heads-up before you start. The hardest move here, actually getting your business into the roundups, reviews, and comparison lists AI leans on, is a project on its own, and it deserves a full walkthrough. That’s the follow-up I’m working on now. For today, start with the audit and your own pages, because that’s the work that pays off no matter how the tools change.

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The Three-Prompt Test

The fastest way to see your AEO reality is what I call the Three-Prompt Test: three questions you ask the major AI tools to find out whether they know you, recommend you, and describe you accurately.

Run these in a fresh, logged-out chat across ChatGPT, Claude, Gemini, and Perplexity:

1. The direct one: “What does [your company] do, and who is it for?”

2. The category one: “What are the best [your category] for [your ideal customer]?”

3. The head-to-head: “[Your company] versus [your top competitor], which is better for [specific need]?”

For each answer, ask yourself three things: Did it mention me at all? Was what it said accurate? Would I have recommended me, based on that answer? If you’re missing from the category question, that’s your biggest gap. If the answer is wrong, that’s a brand-risk problem you can start fixing today by correcting your own pages, because those are the facts the AI checks against.

Here’s what that looks like in practice. Say you run a regional bookkeeping firm. You ask ChatGPT “what are the best bookkeeping firms for small nonprofits in Denver,” and it names three competitors and never mentions you. That’s the category gap, and it’s the one that costs you the most business. Then you ask “what does [your firm] do,” and it confidently describes a service you stopped offering two years ago. That’s the accuracy problem. Neither answer took more than a minute to find, and together they tell you exactly where to start: get mentioned where the AI is already looking, and fix the stale facts on your own pages.

That whole loop, the three prompts plus a way to score what you find, is the engine of an AEO strategy. I built the full version into a free download, the Become the Answer audit: the three prompts, a scoring sheet so you can track what each AI says about you over time, and the complete five-move checklist on one page.

To get the download, subscribe below, it’s free, then reload this page and the download will appear right here. Subscribe free →

Don’t just file this away. Run the three prompts today. It takes ten minutes and it changes how you see your whole online presence.

If you only remember this:

• AEO is the work of becoming the answer AI gives. SEO gets you in the room, and AEO gets you recommended once you’re there.

• Roughly 90% of what AI says about you lives on other people’s pages, so your reputation in AI is built mostly outside your own website.

• The buyers AI sends you are pre-vetted and ready, which makes one AI recommendation worth more than a pile of cold clicks.

So here’s what I’d love you to do. Run the three prompts on your own company, then hit reply or post a Note and tell me what you found. Was the AI right about you, wrong about you, or did it have no idea you exist? I read every one.

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Worth Your Time

A few pieces from other creators I think are worth your attention.

• Kamil Banc — Get AI-Search to recommend your business, correctly. A real audit of a business scoring 51/100 on AI visibility, plus how to get ChatGPT and Gemini to recommend you with the right facts.

• This Week in AI — How AI-First Marketing Looks Like in 2026. Three marketing shifts for 2026, with a full section on Answer Engine Optimization and getting discovered inside AI.

• Kristina God — Is SEO Dead in 2026? The Father of SEO and Google Just Answered That. The man who coined “SEO” weighs in on staying visible as AI answer engines replace the old search game.

Questions Leaders Are Asking

What is the difference between SEO and AEO? SEO (search engine optimization) is the work of ranking your website high on a results page so people click it. AEO (answer engine optimization) is the work of getting your business named inside the answer an AI gives. One earns the click, the other earns the recommendation.

Is AEO replacing SEO? No. SEO is still the foundation, and a lot of what AI pulls from depends on a strong search presence. AEO is a second layer you add on top. The smart move in 2026 is to keep doing SEO and start doing AEO now, while most competitors haven’t.

How do I know if AI recommends my business? Run the Three-Prompt Test. In a logged-out chat, ask ChatGPT, Claude, Gemini, and Perplexity what your company does, what the best options in your category are, and how you compare to a competitor. Note whether you’re mentioned, whether it’s accurate, and whether it recommends you.

How does AI decide which businesses to recommend? AI pulls from many sources at once, and roughly 90% of what it cites comes from third-party pages, not your own site. It favors clear, well-sourced answers, consistent facts across the web, and mentions in the roundups and reviews it already trusts.

Can a small business compete with bigger brands on AEO? Yes, often more easily than with traditional SEO. AI rewards the clearest, most relevant, best-sourced answer to a specific question over raw brand size. A focused small business that answers a narrow question well can get recommended ahead of a larger, vaguer competitor.

Joel Salinas is an AI Strategy Coach for leaders at nonprofits and small and mid-sized businesses. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through the AI Judgment Workshop and the 90-Day Judgment Engagement. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

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Outline

(00:30) – Friends across Substack, a breath of fresh air

(01:23) – Burnout, a tumor, and the business he walked away from

(07:35) – Quality of life vs. lifestyle

(09:07) – Sacred business vs. opportunistic business

(11:15) – Why AI is nobody’s goal

(16:48) – The AI Judgment Workshop and reading your team

(19:18) – The Harmony Map, and slowing down in the AI era

(27:40) – The eco rat race: old identity, new context

(29:46) – We’re all toddlers with AI

(31:48) – “What do you truly want?”

(35:39) – The Golden Sequence: connection, growth, belonging

My Takeaways

Sacred business vs. opportunistic business. So Phil opened with the story I keep thinking about. He built a boutique advertising business doing over $100,000 a month with basically him and a virtual assistant, and it nearly cost him everything, autoimmune problems, a health scare, time essentially bedridden. He walked away because his heart was never in it, and out of that came the distinction the whole conversation runs on.

“When we talk about sacred business, it’s honoring all of who you are, your gifts, your talents, your failures, your victories, and it’s tied to service. The opportunistic business is chasing the trend. It’s not really looking at who am I and what wants to come through.”

When you chase the trend, you spend your life playing catch-up on somebody else’s dream. I’ve watched leaders do it with AI too, grab the shiny thing because everyone else did, and then wonder why it drains them.

AI is nobody’s goal. Here’s where I jumped in, because I see the same thing coaching leaders on AI. I’ll ask why they’re bringing it in, and a lot of the time the honest answer is “we just want to be an AI-forward business.” That’s the trap Phil is describing, and it’s why I keep saying AI is nobody’s goal.

“So much of what I’m doing is just following my own threads of curiosity and wonder, and asking the questions I’m looking to answer for myself, and then allowing people a window into that.”

AI is the means to whatever your goal always was. Marry that goal to something you actually care about and the work stops feeling like burnout, which is the whole discipline behind the AI Leadership Triad. The tool moves you toward what you wanted, it doesn’t get to become the thing you want.

The eco rat race. Phil lived in Costa Rica for three years, and he had the funniest, truest line of the whole talk, about the founders and VCs who burn out, move there to build a wellness center, and change absolutely nothing.

“They bring the same New York energy to the picture. They’re sitting in the most beautiful locations in the world and they’re still the most stressed out people you’ll ever meet. They’re bringing that old identity to a new context.”

nailed it in the chat, you can build a new business with an old identity and hit the same walls.

You can’t relocate your way out of who you are, and you can’t AI your way out of it either.

We’re all toddlers with AI. The thing I keep coming back to is how humble Phil is about all of this. He refuses to look down on anyone’s messy AI use, and he put the reason plainly.

“With AI specifically, we’re all kind of like toddlers. And I include myself in that, and even you, because how far are we really into this? We’re a couple years into this journey.”

I study this for a living and I still bring in a new expert every Thursday, because none of us has it figured out. Phil’s antidote to the noise is one question he and Carolina ask their clients: what do you truly want. Ask it enough times and it gets dangerous in the best way, because it’ll take you down a rabbit hole if you let it. That question is the human work AI can’t do for you, and it’s the same reason I keep telling leaders not to outsource their thinking to it.

Reading your team before you buy the tool is exactly what I run leaders through in my AI Judgment Workshop, which is open for registration now. If you’d rather go deeper one-on-one or with your organization, just let me know!

One Question to Sit With

Phil ended on the question I want to leave you with too: what’s the why behind where you’re putting your hours, and does it marry where your passions lie?

Watch the full conversation above for how Phil and Carolina help people sit with that. And when you’re done, go subscribe to Sacred Business Flow.

About Phil Powis

Phil Powis is the creator of Sacred Business Flow, a Substack with more than 33,000 subscribers that he writes with his partner Carolina. He teaches founders and creators to build what he calls a sacred business, one that honors who you are and stays profitable, instead of an opportunistic business that just chases the trend. His work pairs business and marketing strategy with inner work, including the Harmony Map assessment, which reads what he calls your nine fundamental frequencies and points you to the one “breakthrough frequency” worth building around. Subscribe to his writing at Sacred Business Flow, and take the free Harmony Map assessment at sacredbusiness.com.

Sacred Business Flow

Clarity, visibility, clients. A business that feels as good on the inside as it looks on the outside.

By Phil Powis ❤️⚡️

About me

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through a monthly live workshop and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.

Written by a human, for humans.

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So here’s the pressure a lot of us are feeling right now: put AI into everything, and do it fast. Nobody wants to be the team that fell behind, so we bolt an AI feature onto the product and figure we’ll make it safe and useful later.

Here’s the thing though. Most of what decides whether an AI product actually works, and whether it’s safe to put in front of people, gets settled before anyone opens the design tool. That’s the part we skip. It’s also a big reason so many AI implementations fail.

This week I’m handing the newsletter to someone who works right in that gap. is a product strategist and UX and AI designer (UX means user experience, how a product actually feels to use), and the founder of Creative Glue Lab, where she helps teams design and build AI products. I liked how she frames this... not as a compliance headache to clear at the end, but as a set of questions you ask up front, before the decisions harden into code.

What she’s put together is a practical checklist: 8 areas that decide whether an AI product turns out useful, safe, and compliant. If you build products, commission them, or just have to approve them, this is the pre-flight list worth keeping.

UX + AI

Build better experiences in a world shaped by AI. Practical insights at the intersection of UX, AI, and Product Strategy.

By Ileana

If her thinking resonates, subscribe to her Substack after you read. I’ll meet you at the bottom with the one area I’d start with.

Over to Ileana.

New Material, New Responsibility

We’re building with a new material.

AI went from a tech demo to the thing we shape into everyday products: chatbots, AI agents, autonomous workflows, and intelligent systems.

It’s new for everyone. And the mood feels the same in most rooms: excitement + speed.

No one wants to be the team that fell behind, so the pressure is to move fast and add AI into everything. We’re being asked to build more, faster, while working with higher complexity and higher risk.

AI products remember, decide, and act on someone’s behalf. They shape what people trust, choose, think, and the calls a business makes. That capability comes with a new level of responsibility for the teams designing and building it.

Responsibility demands clarity.

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And clarity needs questions.

That’s a new level of responsibility, and most of it gets decided before anyone touches the interface. With AI, UX reaches well beyond how a product looks, into data, safety, and compliance.

Use this guideline to cover what you need to build useful, safe, and compliant AI products.

Designing AI Products: 8 Areas to Get Right

This guideline walks you through 8 areas that make or break the experience, the ones AI adds on top of UX fundamentals like accessibility. Some will matter more than others depending on what you’re building. Cover the ones that do, and always bring the right expertise into the room.

1. Start with fit, not capability

Should this be AI at all?

AI earns its place when it improves the experience in a way a simpler pattern can’t. Fit also means the kind of AI and the relationship it creates with humans.

What to cover:

• What problem are we solving, and what does AI make better: speed, quality, access?

• Would a simpler rule, workflow, or automation solve this better?

• What kind of experience is this: does the AI assist, act on someone’s behalf, or work in the background?

• Which model or approach fits, and what does it cost in accuracy, latency, and risk?

• What relationship are we building between humans and AI?

Look for a real reason to use AI: it should solve the problem better than the simpler option.

2. Define boundaries before behavior

What could go wrong, what should the AI never do, and who does it affect?

AI changes the shape of product risk: part of the behavior is generated in the moment, so the system can respond in ways no one fully scripted.

What to cover:

• What should this AI never do, and where could it overstep its role?

• Who uses the AI, and who is affected by its output or action?

• What happens if it’s wrong, biased, or overconfident, and people trust it anyway?

• Can people tell why it did something, check it, or undo it?

• Could one failure trigger another?

Look for clear boundaries: the team should know what the AI is allowed to do, what it should avoid, and where the stakes change because of the context.

3. Treat data as part of the experience

What does the AI know, and should it?

AI experiences run on data, and that data shapes what the AI can produce, reveal, or get wrong.

What to cover:

• What data is the AI using, and where does it come from?

• Is it personal, sensitive, biased, or outdated?

• Can people see, correct, or delete the context it’s using?

• Is the data safe to send to this model or vendor, and what should never be context?

Look for a clear data boundary: people shouldn’t be surprised by what the AI knows, remembers, assumes, or exposes.

4. Design for verification, not just output

Can people understand and verify what the AI produced?

AI output can sound confident while being wrong, so the experience has to help people judge how much to rely on it.

What to cover:

• Is the output a draft, a suggestion, a recommendation, or a decision?

• Can people verify it, see sources, or compare options?

• Does the system show uncertainty, or refuse when it shouldn’t answer?

Look for calibrated trust: the product shouldn’t make AI output feel more certain, final, or authoritative than it deserves.

5. Protect human agency over time

What does this do to people over time?

AI products affect more than task completion. They shape confidence, judgment, dependency, and behavior over time.

What to cover:

• Does this make people more capable, or more dependent?

• Does it support their judgment, or replace it?

• What does it do after weeks or months of use?

• What are the hard moments: confusion, rejection, failure, or no fix available?

Look for a healthier relationship over time: people keep their own judgment and skill, rather than handing them to the AI.

6. Make autonomy controllable

Can people steer, pause, approve, undo, or override it?

Control matters more as AI becomes more autonomous. Suggesting is one thing; acting on someone’s behalf is another. The aim isn’t to approve every step, that defeats delegation.

What to cover:

• What can the AI do alone, and what needs approval?

• What can be previewed, edited, or undone?

• Can people pause, stop, override, or redirect it mid-task?

• Is review calibrated to risk, or does it create approval fatigue?

Look for meaningful control: people can see what the AI is doing, step in before it’s hard to undo, and recover after, without signing off on every move.

7. Set expectations at the point of use

Can people tell what it’s doing, and what shouldn’t they assume?

Transparency is about showing the right information at the right moment.

What to cover:

• Do people know when AI is involved, and what’s AI-generated?

• Do they know what data or context was used, and what changed?

• Do they understand whether the AI is suggesting, deciding, or acting?

• What might people wrongly assume, and what limits need to be visible?

Look for clear expectations: people know what the AI is doing, what it isn’t, and where their own judgment still matters.

8. Plan for misuse, compliance, and ownership

What needs to be protected, monitored, recovered from, and owned after use?

AI connects language, data, tools, and permissions, which opens new safety, misuse, and compliance questions that should show up early.

What to cover:

• Could the system be manipulated through prompts, files, or connected tools?

• Could sensitive data leak, or are permissions too broad?

• Could people misuse it for fraud, manipulation, or harmful content?

• Where does a human step in, and what happens when it causes harm or confusion?

• Who monitors, supports, and owns the system after launch?

Compliance isn’t a checkpoint at the end, or a “please double-check” disclaimer. It needs to be designed for from the start. In the EU, the AI Act is phasing in, with transparency rules for AI-generated content from 2 August 2026 and stricter rules for high-risk systems.

Look for readiness: the product should be safe enough to use, clear enough to explain, compliant enough for its context, and supported enough to improve when real-world failures appear.

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Thank you, Ileana!

So which area do you start with? If you made me pick one, it’s the first: fit before capability. Almost every AI product mess I’ve watched traces back to a team that reached for AI because it was available, not because it was the right tool for the job. Get that one honest and the other seven get easier.

One question to sit with: where in your product is AI solving a problem a simpler rule could solve better?

FAQ

What tools do I need to design a safe AI product? Fewer than you’d think. This checklist is tool-agnostic. You need a way to write down decisions (a shared doc works), the people who understand your data and legal exposure, and whatever design and prototyping tools you already use. The 8 areas are about the questions you ask, not the software you buy.

How long does it take to work through these 8 areas? A first pass on a single feature can take an afternoon with the right people in the room. It’s front-loaded work: a few hours of hard questions before you build saves weeks of rework, incident response, and trust repair after launch. Not every area applies equally, so cover the ones that matter for what you’re shipping.

Do I need to be a designer to use this checklist? No. Ileana wrote it for teams, not just designers. Product leads, engineers, founders, and the executives approving an AI feature all have a stake in these questions. If anything, the areas most people skip (boundaries, data, compliance) sit outside traditional design work entirely.

What’s the most common mistake teams make when adding AI? Starting with capability instead of fit, reaching for AI because it’s available rather than because it solves the problem better than a simpler rule. The second most common: treating compliance and verification as things to bolt on at the end, when the article’s whole point is that they have to be designed in from the start.

How do these 8 areas apply to a small team or a non-product company? They scale down cleanly. If you’re a nonprofit adding an AI chatbot or a small business automating a workflow, you still decide fit, boundaries, data handling, and who’s accountable when it’s wrong. The stakes are the same even when the team is one person. Skip the areas that don’t apply and cover the ones that do.

Which of the 8 areas should I start with? Start with fit (area 1). If AI isn’t the right tool for the problem, the other seven areas won’t save the product. Once fit is honest, move to boundaries and data, since those shape everything downstream. Compliance and ownership are last to finish but should be considered from the first conversation.

Written by a human, for humans.

About Ileana Marcut Ileana Marcut is a product strategist, UX and AI designer, and founder of Creative Glue Lab, where she helps teams design and build digital products and AI solutions. She writes about designing with and for AI at her Substack.

Joel Salinas is an AI Strategy Coach for leaders at small and mid-sized businesses and nonprofits. AI is everywhere; judgment is scarce. Joel helps leaders adopt AI without outsourcing their judgment to it, through live workshops and 90-day engagements. Creator of the AI Leadership Triad. He writes Leadership in Change.