I took a few weeks to step away from the noise and focus on what's actually working in AI workflows versus what's just filling up LinkedIn feeds. What I found changes how I'm approaching everything.

Most AI thought leadership right now is philosophical speculation dressed up as business strategy. I'm more interested in the practical reality of how AI actually impacts teams and workflows today.

Starting today, you'll see two newsletters per week as I share the breakthrough workflows I've been testing. Coming soon: Midjourney techniques that are transforming creative teams, advanced writing processes that compound your best ideas, and where agent platforms like n8n, Gumloop and Lindy actually deliver value.

Today's focus: deep research workflows that supercharge teams and preview the agentic future.

The leaders building real competitive advantage aren't just prompting AI for quick answers. They're developing systematic research processes that compound expertise and reshape how knowledge moves through their organizations.

This isn't just about better workflows. It's a thought exercise for what's coming. The deep research capabilities you build today mirror what agents will automate tomorrow.

If you're still thinking of AI as a productivity hack, what follows will shift your perspective toward building the foundation for autonomous knowledge work.

The Moment I Stopped Googling

I used to burn hours clicking through shallow search results, skimming recycled articles just to cobble together basic context for strategy documents.

Research wasn't intellectually challenging. It was just slow. And that killed momentum for everything I was trying to build.

The breakthrough came watching OpenAI's Deep Research tool at work. That loading bar ticking by while it went off to fetch structured, source-backed information from across the web.

There's something psychologically powerful about outsourcing that legwork—suddenly I had an invisible research assistant running in the background.

This wasn't prompt-and-return like basic ChatGPT. Deep research tools interrogate problems, gather actual sources, and come back with current, relevant context. Like having your own mini-Wikipedia delivered on demand, complete with citations instead of AI hallucinations.

The first real test: I wanted to know if those "AI agents run the world now" headlines were legit or just hype. Instead of spending my afternoon diving down rabbit holes, I fed the question to deep research and walked away.

Twenty minutes later, I had real company examples, recent implementations, specific use cases, and nuanced analysis I never would've found skimming Google's top hits. Suddenly I could back my writing with genuine, current evidence instead of running on gut instinct.

Here's what changed everything: I don't just run deep research once. I feed results back into further prompts, chaining them to create layered insight. What used to take a week now happens in a day.

Insights build on insights, compounding fast.

Why This Is Really Agent Training

Building deep research workflows isn't just about better information gathering. You're training for a fundamentally different way of working.

Every time you prompt deep research and watch it disappear to fetch, analyze, and package information, you're practicing agentic work. Learning the muscle of giving autonomy to machines—not just using a tool, but delegating an entire process.

Most people don't realize it yet, but prime model agentic workflows are rolled out piece by piece. Today's research tools are early glimpses of what's coming: AI that runs its own mini-processes, makes decisions, and returns structured output without constant hand-holding.

The manual chaining we do now will soon happen automatically. True agentic AI will run dozens of workflows simultaneously, linking them together without you touching a thing.

The real shift isn't technological. It's psychological.

Organizations starting today will adapt smoothly when agents handle entire knowledge cycles autonomously. The rest risk being blindsided.

Think about your future team composition. Digital teammates will likely outnumber human ones within a few years. The workflows you delegate today, the experiments you run, the comfort you build with AI autonomy. These habits will pay off exponentially as the technology accelerates.

You're not just learning new tools. You're learning how to scale up with AI instead of being replaced by it.

What Deep Research Actually Is

Let's be clear about what we're talking about.

Every major AI platform—ChatGPT, Claude, Perplexity, Google Gemini—now offers some version of "deep research" or "extensive research." The names vary, but the function is consistent.

This isn't just search with extra steps. You ask a complex question, and the AI scours the web, gathers real sources, distills key findings, and packages everything with citations. Like having a research assistant who actually knows how to synthesize information instead of just dumping links on your desk.

In practice, it's as simple as hitting a button in your chat window.

I use ChatGPT's deep research almost exclusively now—the O3 model is my go-to. But every major tool performs at a genuinely useful level. Some are faster, some more readable, some handle certain queries better.

Find the feature in your preferred platform and test it. They all work. The differences matter less than building the habit of actually using them.

My Workflow: Experience First, Research Second

My process doesn't start with research. That's backwards.

I usually begin by answering questions from personal experience, intuition, and years of pattern recognition. That's my 70% foundation—the insights I've earned through actually doing the work.

As I write or build a strategy, weak spots reveal themselves. Statements that feel thin. Claims I can't back up. Areas where my confidence dips. That's when I deploy deep research—not before.

I don't shotgun research across everything. I zero in on specific gaps where I need reinforcement.

Think of it like building a wall. My personal insights are the bricks and deep research is the mortar that binds them into something solid and defensible. Sometimes it flips: research becomes the brick, experience becomes the glue.

Here's where most people mess up: they treat this like a linear process. Fetch research, paste results, move on.

Wrong.

It's a feedback loop. I take what deep research surfaces and use it to prompt new questions, challenge assumptions, validate hunches. Each pass sharpens the insight. The real edge comes from chaining research and using each output as fuel for the next question.

You know you're done when your logic feels structurally sound. When every weak point has supporting evidence or clear, defendable rationale. You could always go deeper, but you've reached "enough" when the foundation is strong, not just patched.

Most people stop too early. They get one good answer our of the LLM agent and call it research. The competitive advantage lives in that second and third layer of inquiry.

Case Study: Cutting Through Agent Hype

As I mentioned, I recently kept seeing headlines about "AI agents running businesses end-to-end." The kind of breathless coverage that makes you wonder if you're missing something or if it's just vendor hype dressed up as breakthrough news.

I wanted the real story.

Instead of falling down the Google rabbit hole, I fired up deep research in ChatGPT and asked:

Find recent, real-world examples from the past 6 months of companies actually using AI agents to automate substantial business operations—not just pilots or isolated use-cases, but actual deployed workflows. What did they automate, what tools did they use, and what results did they see?

Twenty minutes later, I had concrete case studies with sources:

A SaaS company using AI agents to handle 80% of customer onboarding without human intervention—complete with implementation details and success metrics from the CEO's own interview.

A logistics startup chaining agents together to automate freight booking—with actual cost and time savings reported in their Series A announcement.

A mid-sized marketing agency using agents for campaign setup, asset distribution, and reporting—sourced from a detailed case study they published, not just a press release.

Each example came with links to original documentation, interviews, and data. Not third-party summaries or recycled takes from the same three industry blogs.

This wasn't just faster than my old approach—it was better. Instead of managing browser tabs and cross-referencing sources, I got structured context and recent data I could actually trust.

The result? My next article and the advice I gave to people was grounded in real implementations, not industry noise. That's the difference between research that compounds your expertise and research that just fills time.

Start Small, Build the Habit

Like I said, every major LLM now has a "research" or "deep research" button. If you're not using it, you're leaving value on the table.

There's no secret handshake. It's sitting right in front of you.

The real move isn't finding the perfect use case—it's using deep research constantly. If you're not maxing out your monthly usage limits, you're not exploring the edge of what's possible.

Reps are everything.

Top operators use deep research five, six, ten times a day. They're not saving it for "important" projects. They're building muscle memory on everything from competitive analysis to market research to fact-checking their own assumptions.

Push the tool until you hit its limits. That's how you learn both strengths and weaknesses before it matters. Encourage your team to do the same. This isn't about individual productivity, it's about leveling up the whole organization.

Want to know if your deep research result is actually better than Google? Gut-check it the way you would if a team member handed you their work. Does it surface something you couldn't get from a lazy search, or is it just rehashed noise from the same top-ranked articles?

You'll know.

There's no special workflow or secret trick here. It's about building confidence through daily use. The tools are still cheap—now's the time to practice before pricing or access changes.

These repetitions aren't just about immediate productivity. They're training you and your team for when these tools become true agents, chaining complex workflows together automatically.

You don't want to be the leader scrambling to catch up when that future hits.

Build the habit now. Make it survival later.

The Copy-Paste Trap

The biggest mistake I see with deep research tools? People treat them like a copy-paste shortcut.

It's the modern version of submitting a Wikipedia article as your own work. Just as lazy. Just as obvious.

Dumping raw AI output into a report or sharing it directly with your team signals complete lack of respect for their time and intelligence. If you're not distilling and re-contextualizing insights, you're not leading—you're mailing it in.

Clients and teammates can smell bot-generated copy from across the room. Generic phrasing, surface-level analysis, insights that sound impressive but lack real depth. You risk your reputation fast when you rely on unfiltered output as your final product.

People want your perspective, not regurgitated facts.

Here's what too many leaders miss: AI research is a springboard, not the finish line. The real competitive advantage comes from using these tools to jumpstart your process, then layering your own expertise, narrative, and context on top.

You still have to do the work.

Deep research should cut the time you spend fetching information, but you can't skip synthesis. You can't skip connecting the dots. You can't skip making it yours. The moment you try to shortcut those steps, you lose everything that makes your output valuable.

We’ll start seeing many teams lose credibility because they got lazy with the "glue"—the human layer that turns research into insight, insight into strategy, strategy into action.

You can move faster with these tools. But you can't skip the parts where your judgment, voice, and accountability actually matter.

That's not just a workflow mistake. It's a leadership failure.

Scout Report

Here are two small model updates that have me excited this week:

🔗 Claude Projects Handle 10x More Content → Anthropic just made Claude way more usable for heavy workflows. Projects now support 10 times more content, and the new voice mode puts it closer to the top LLMs. → Read more

🔗 ChatGPT’s Advanced Voice Mode Just Got an Upgrade → The latest release notes (June 7) detail upgraded intonation, realistic cadence, better emotional expressiveness (sarcasm, empathy), and even built‑in translation. → Read more

Forward Signal

If you're not already using deep research workflows, I hope this opened your eyes to the competitive advantage sitting right in front of you. Start incorporating these processes now.

But here's the bigger picture.

This is just one step toward true agentic AI. What you're learning today—sending AI off to research and return with structured insights—is training you for a fundamentally different way of working.

Eventually, autonomous AI will chain these processes together automatically. Research connects to analysis, analysis feeds strategy, strategy informs execution. All without your prompting.

The framework matters: get comfortable with AI agents doing meaningful work and coming back with genuine value. When we start chaining all of this together, that's where you'll see the real magic in scaling organizations and reshaping what teams look like.

Start with deep research. Think like an agent architect.

Next up later this week: the innovation coming out of Midjourney that's transforming creative output for design teams. Some of these capabilities will surprise you.

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Are AI Agents Just Expensive Vapor?

You're going to hear a lot about "AI agents" this year.

Vendors will pitch you dreams of cutting 50% of your workload. Peers will claim their businesses have agents running everything but the coffee machine. I've seen these demos. They're mostly fantasy dressed up in a slick interface.

Let's be honest: Everyone's using some form of AI, but most businesses aren’t running truly agentic systems at scale yet in mid-2025.

What people call "agents" often turns out to be basic chatbots wearing fancy hats, copilots that still need your hands on the wheel, or workflow hacks that require more babysitting than my neighbor's toddler. The tech is evolving fast. The definitions are blurry. Most organizations haven't even fully digested the fundamentals.

Brutal.

Here's the reality check you won't hear at conferences: If you haven't integrated the core models and gotten your team comfortable with manual prompting and basic workflows, you're not behind on agents. You're behind on the basics.

The "agent" wave is absolutely coming, and yes, competitive teams will move quickly as the tech matures. But you're not late to the party unless you're ignoring the main models and the foundational adoption curve altogether.

Before we dig into what to implement and what to avoid, let me cut through the fog about where things stand and what "agentic" tech really means.

The Truth About Today's "AI Agents"

Don't believe the hype. Most businesses are not deploying true AI agents today. Not even close.

These tools dominate headlines and investor pitch decks, but practical, wide-release agent tech is still rare. Especially outside the U.S. and for smaller organizations. Even OpenAI's "Operator" remains U.S.-only beta with external access more exclusive than a secret underground club in Tokyo. I had to pull strings just to get a demo.

We're standing on the edge of a wave, not surfing in the middle of it. Competitive businesses will adopt quickly as agentic tech matures, and being prepared now is how you avoid getting left behind later. The real value today isn't in chasing vendor promises. It's in understanding what's possible versus what's still digital vapor.

You need a mental model for this space, so let me break down four key categories of AI tools that everyone confuses:

1. Raw Models (The Engine)

Examples: OpenAI, Anthropic, Microsoft, Google, open-source LLMs.

Think of these as the industrial engines powering everything else. They're usually accessed through APIs, built for technical teams who want full control over every lever and dial. They offer high flexibility but aren't for most SMBs since they require engineering resources and a tolerance for complexity. Tech teams love them; but marketing teams might rather stick forks in their eyes than work with raw APIs.

2. Official Interfaces (The Workhorse)

Examples: ChatGPT, Claude.ai, Gemini, Grok.

These are your Honda Civics of AI. Reliable, user-friendly interfaces designed for non-technical users and teams. This is how most people actually "use AI" today. Great for business experimentation and daily productivity, but bound by the vendor's rules and limitations.

Right now we’re seeing companies try to build entire workflows on these interfaces, which is like trying to run a construction company with only consumer-grade tools from Home Depot. Possible, and practical, but might not be ideal as you scale.

3. Wrappers & "Agents" (The Middle Layer, Often Overhyped)

Examples: Perplexity, Notion AI, browser agents, niche startups.

This is where the marketing gets ahead of reality. Most third-party tools are wrappers: they combine the raw models with their own interface or workflow. Many now brand themselves as "agents." Usually just automated workflows with limited autonomy, like a restaurant hostess who can seat you but can't take your order or bring your food.

True agents take action with minimal prompting, automate tasks, and begin to act independently. They're like having an invisible assistant who joins your meetings, takes notes, distributes action items, and follows up on deadlines. All without you having to say "hey, could you..."

These are still rare in the wild, despite what your LinkedIn feed suggests.

4. Enterprise Solutions (The Heavyweights)

Examples: Salesforce Einstein, Microsoft Copilot, vertical-specific platforms.

The corporate battleships. Slow to turn but hard to sink. These offer deep integration, security, and scale, but they're expensive and risk lock-in. Best suited to operations-driven teams needing stability and compliance. For most SMBs, these are overkill or simply financially out of reach, like buying a combine harvester for your backyard garden.

Where most people get confused:

Many tools blur these lines faster than a watercolor painting. Wrappers claim to be agents, enterprise solutions rebrand as "AI platforms," and the raw model leaderboard changes weekly like a game of musical chairs. One week Google's Gemini leads, the next week OpenAI drops a new model, then Claude leapfrogs them both. Don't get distracted by the horse race. It's like following sports standings when you should be practicing your own game.

What's actually agentic right now? Practical examples include meeting bots that autonomously join calls, transcribe, summarize, and distribute action points through email to participants without constant human prompting.

Consumer agents like OpenAI’s Operator can handle basic tasks like booking and scheduling, but they're not fully autonomous or widely accessible yet. Plus, most people are still afraid to ask agents to book a haircut without it trying to order a pizza at the same location.

True agentic behavior means tools that act, decide, and report back without any prompting or micromanagement, and they're still unicorns in today's market [in mid-2025].

The takeaway for SMBs and mid-sized teams: Stick to the official interfaces or well-vetted wrappers for now. Pick a platform that fits your workflows, and don't get FOMO over "agents" that don't yet deliver.

Start monitoring agentic developments with an eye for what will actually move the needle for your operations, and be ready to move when the tech gets real. The revolution is coming, but it isn't quite here yet.

Smart Selection: Tools That Work Now and Won't Box You In Later

Stick with the Big Platforms—But Keep Your Options Open

ChatGPT, Claude, Gemini, and Grok are the safest, most capable user interfaces today. I've kicked the tires on all of them, and they each have their strengths. No one model or interface "wins" for long in this field. It's like trying to crown a permanent champion in a sport where the rules change every quarter.

Pick an ecosystem that fits your workflow and budget, knowing each will push agentic features as soon as they're viable. Trust me, you’ll know when these agents are available—they want your money.

If you have the resources, experiment with more than one platform, but avoid scattering your data and processes across too many systems like I did earlier this year. I ended up with notes in five different AI interfaces and couldn't find anything when I needed it.

Lesson learned.

Don't Buy Hype—Buy Fit for Your Real Problem

Identify the most repetitive, high-friction tasks in your team's day. For a finance team, it might be processing receipts and expense reports. A mind-numbing time sink that everyone hates. For sales, it might be meeting notes and follow-ups. For support, it might be categorizing tickets.

Look for tools or wrappers that automate those specific pain points. Don't chase "agents" for their own sake. That's like buying a Ferrari to drive to the grocery store. Most "agent" startups are immature or unproven. Use with caution and demand evidence before investing. You don’t want to get burned by three different "AI agent" startups that promise the world and deliver a paper map.

Spotting the Red Flags in Today's 'Agent' Products

Before you adopt any new tools or systems, let’s talk about where things go wrong—and how to dodge the most expensive mistakes.

🔴 Marketing Hype ≠ True Agency

Most "AI agents" marketed today are just chatbots or copilots with a new label. True agents act autonomously and execute multi-step workflows without constant hand-holding.

Ask yourself: Can this tool take actions, adapt, and achieve goals with minimal human nudging, or is it just a fancy prompt engine wearing a bow tie? Most production "agents" in mid 2025 are still far from the real thing, despite the marketing fairy tales.

🔴 Security Risks and Custom-Build Pitfalls

Security and Compliance: Agents with access to core business systems are a security risk if not handled properly. It's like giving an unknown contractor keys to your office, server room, and financial records. If security isn't front and center in their pitch, walk away. Gartner predicts agent abuse will drive a significant chunk of data breaches by 2028.

Custom Builds and Complexity: Building your own agents is a money and time pit for most SMBs. Romantic in theory, painful in practice. Most early-stage agent failures we’ll see in 2025 will come from biting off too much. Teams underestimate the complexity, the edge cases multiply like rabbits, and suddenly your "six-week project" is in month eight with no end in sight.

🟠 No (or Weak) Integrations

If the tool you want to automate doesn't have open APIs or integration hooks, agents can't do their job. It's like hiring a personal chef but locking your kitchen door. Avoid products built on "walled gardens" or closed platforms; these are dead ends for automation.

Most SMBs should look for solutions built to plug-and-play with their existing stack, not ones that demand you rebuild your entire digital infrastructure just to accommodate them.

🟠 Bad Data = Bad Agents

Agents are only as good as the data you feed them. If your internal systems are messy, siloed, or lack governance, you're setting up for frustration or failure.

Don't expect magic from agents until your data is clean, accessible, and up to date. No AI can turn your digital landfill into gold. Not even the most expensive ones.

🟡 No Clear Goals = No ROI

Don't invest in agent tools just because "AI is hot right now" or your competitor mentioned them on LinkedIn. Focus on defined, repetitive workflows with clear pain points and measurable impact.

Start with a simple goal: reduce the time our support team spends categorizing tickets by 50%. That’s specific, measurable, and meaningful.

Start small, measure obsessively, and implement an agent service only when it can demonstrate real results. The best implementations build momentum through visible wins, not grand visions.

🟡 Lack of Transparency Breeds Mistrust

Prep your team thoroughly too: agents are productivity multipliers, not job killers. Frame them as taking the robotic parts out of human work, not taking human jobs. How you message this internally matters more than most leaders realize.

The agent revolution is a no-brainer. It's coming. But right now, we're in the messy middle, where buzzwords outpace reality and the most impressive demos often hide the most fragile implementations.

Avoid shiny objects and stay focused on tools with genuine autonomy, robust integrations, error handling, and a clear path to value. Clean up your data like you're expecting company, set measurable goals your CFO would respect, and look for vendors who understand SMB realities instead of enterprise fantasies.

This market moves faster than fashion trends. Be skeptical, start small, and keep humans in the loop. You don't need to be first in line for every new platform, but you do need to be ready when the truly transformative ones mature.

And if a vendor tells you their agent system is "fully autonomous" and "never makes mistakes"?

Show them the door.

In the AI world, honesty about limitations is the first sign of competence.

Scout Report

Here are the two signals I’m recommending this week—big picture trends and real talk on where AI and agents are actually headed.

🔗 Visual Timeline: AGI by 2027 → This visual timeline doesn’t pull any punches. It’s a fascinating, sometimes bleak, visual of where agents fit in and how fast things will accelerate. → Read more

🔗 Video: Are Your Job Titles on Borrowed Time? → If you like the Diary of a CEO, this is a great debate. Sure, it’s a little overhyped, but there’s real insight here about agents, automation, and the future of work. → Watch video

Heard on NotebookLM

This week, I'm sharing something straight from my workflow—a custom audio clip generated using NotebookLM.

If NotebookLM isn't on your radar yet, it should be. I'll go deeper on my research tool stack soon, showing how to build your own custom audio summaries using deep research without learning a complicated new workflow. That's coming in next week's newsletter.

For now, I've created an audio summary using all the research behind this week’s newsletter. If you're still fuzzy on what AI agents actually are (and why they're suddenly everywhere), this clip delivers the straight goods.

Forward Signal

I hope this gave you a sharper sense of where agentic tech actually stands. As you've seen, even the definition of "AI agent" is all over the map, and right now, implementation is complicated for most teams.

But here's what I see coming: agent engineering is about to become its own specialty. Over the next year, expect to see a wave of new roles—engineers dedicated to setting up, maintaining, and evolving these agentic systems.

The bottom line: most teams won't implement agents without technical talent, and that's a real opening—AI isn't just about automation, it's also creating new opportunities (and new job titles) for people who can bridge the gap.

That’s it, that’s all! Look for my upcoming piece on research workflows—it's shaping up to be my most actionable yet.

If this edition of Frontier Notes for AI Leaders gave you great insight, please share it with someone who leads through change. And if you haven’t subscribed or upgraded to premium yet, we’re delivering frameworks and systems that make AI practical every week—sign up below.

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The shift from Remote Ground came from listening to you. The conversations that resonated most deeply were about AI transforming leadership in real time. You engaged when I explored how these tools reshape decision-making, creativity, and team dynamics.

Today's issue explores something that has fundamentally changed my workflow: voice as a new interface for thought. I've transitioned to voice-first for nearly everything. Strategy documents, creative briefs, even this newsletter.

What surprised me wasn't just the efficiency. It was how this shift altered my relationship with creation. And for work requiring original thinking (critical in an AI-accelerated world), this subtle shift has profound implications.

If you're still typing everything, what follows might transform how you work.

The Breakthrough Wasn't a Demo

I didn't realize voice would upend my entire workflow until I was coming back from a working off-site, two hours into a four-hour drive home.

Ideas had been bouncing around in my head all day—concepts I was excited to explore when I finally got back to my desk. But sitting there behind the wheel, I realized I didn't have to wait. I paired ChatGPT's voice mode to my car's Bluetooth and started a conversation to explore those ideas.

I just started talking. Thinking out loud about project frameworks, personal branding, and next steps. After nearly two hours of this back-and-forth with the AI voice assistant, I realized something wild: I wasn't just brainstorming—I was working. No laptop. No notes. Just pure velocity.

When I got home, every word I'd spoken was captured and ready to use. That changed everything. This wasn't just thinking out loud anymore. I was drafting, refining, creating. While driving. This is how voice-first tools quietly eat legacy workflows.

I now draft almost everything through voice. Thirty minutes of spoken thought equals a first draft that actually sounds like me. The tone, rhythm, personality all come through naturally in ways typing never delivered.

Voice Isn't Just Faster—It's Freer

The real power isn't just in the speed. It's in the freedom.

When I speak out loud, I don't polish. I don't posture. I don't perform. The thoughts spill out messy, but they're authentically mine. Emotional, energetic, unfiltered. That's where the gold is.

The Science Behind Speaking

This isn't just my subjective experience. Research backs it up: a 2024 survey by TELUS Digital found 35% of users report speaking is faster than typing. In controlled studies, students using speech-to-text wrote more fluently with fewer mistakes—all while experiencing lower perceived mental effort than when writing by hand.

Voice unlocks your inner frameworks. When I'm riffing out loud, the best ideas sneak out when you're not trying so hard to sound smart. There's cognitive science behind this. Researchers have found that verbalizing thoughts activates different neural pathways than typing. As one cognitive review notes, speaking aloud actively contributes to metacognition and reasoning.

The philosopher Kleist observed this centuries ago: "speech is a creative process that in turn generates thought."

And here's the beauty: the mess doesn't matter because the model catches it. I don't worry about tangents or "ums." I just speak, then tell the LLM to clean it up and find the insight. No blinking cursor judging your next word. No backspace key begging for edits. Just momentum.

Breaking Free From Your Desk

This shift breaks the tyranny of the desk completely. I draft newsletters from a chair overlooking the lake. I brainstorm product strategy on walks. I work through complex ideas while driving. This isn't just a new interface. It's a whole new environment for creation.

Of course, voice isn't perfect for every situation. In shared spaces, it can be impractical. Some people feel self-conscious speaking to devices around others. And for complex editing, a keyboard still wins. But these limitations are shrinking as technology advances and social norms evolve.

Why Smart People Are Still Sleeping on Voice

If voice is so transformative, why isn't everyone doing it? The answer reveals a fascinating blindspot in how we adopt technology.

Most people tried voice workflows too early and got burned. The old tools were clunky. Transcriptions were slow. The assistants weren't that smart. So even high performers wrote them off and haven't revisited them since.

This skepticism shows in the data. The same TELUS Digital survey from August 2024 found only about a third of users incorporate voice into their workplace tasks. The majority—56% of respondents—still limit voice tech to personal use only. We're in a transition period where attitudes haven't caught up with capabilities.

The Tech Has Quietly Leapt Ahead

But here's the truth: the tech has quietly leapt ahead. We're not talking about Siri or Alexa anymore. We're talking about full-scale LLMs with real-time voice input that instantly distill what you say into usable output. Modern models like GPT-4o handle speech with sub-300ms latency, essentially eliminating the lag that made older voice tools frustrating. It's here. It works. And it's damn fast.

The new bottleneck isn't the tech. It's you. Specifically, your perfectionism. Your muscle memory. Your belief that anything not typed and visually reviewed can't be "real" work. That's a lie. And it's costing you hours.

Think about this shift practically: Voice-first workflows don't replace your desk. They front-load it. I use voice to conquer 80% of a task while I'm walking, driving, or between meetings. Then I sit down, clean up the 20%, and I'm done. This is the multiplier.

If I had this three years ago, I'd be twice as far along. I don't say that lightly. This one shift, getting comfortable talking to machines, cut my task time in half. And I'm still getting faster.

Here's the most profound change: For the first time in my life, I'm creating faster than I consume. Let that sink in. I can now produce content at the same quality bar as the books and articles I read—but I can create it faster than I can read them. That's an incredible inflection point. It used to take effort just to keep up with inputs. Now I'm outpacing them—and using that surplus to ship more, refine faster, and actually enjoy the process again.

You Don't Need to Be a Good Talker

The resistance I hear most often comes from a place of insecurity: "I'm not good at thinking on the spot."

Neither was I. Most people aren't. But voice workflows aren't about delivering perfect TED Talks. They're about getting out of your own way long enough to capture what you actually think.

You don't need to sound polished or professional. That mindset kills momentum. The model doesn't care if you ramble or pause or throw in three false starts. It listens, distills, and surfaces what's useful. Then it lets you refine.

This technology filters the mess for you. Forget the old voice-note experience that dumped 15 minutes of unstructured chaos into a doc. The new tools cut the noise, keep the gold, and give you a first draft that's ready for sculpting.

Better With Every Session

The secret benefit nobody talks about? You will get better the more you speak. Not just faster, not just more fluent. Better. Clearer. Sharper. You'll start to notice you're thinking in frameworks. You'll hear better metaphors come out. You'll build fluency on the fly.

Psychology research supports this experience. Studies show that talking through problems enhances higher-order thinking, not just motivation or memory. There's evidence that speaking aloud can reduce "brain lag," letting ideas flow more quickly—similar to how brainstorming out loud often sparks insights that wouldn't emerge through silent typing.

The irony becomes apparent quickly: Voice will make you a better talker in all contexts. You'll start noticing the shift in meetings, brainstorms, pitches. When the moment calls for clarity, you'll have the reps. And everyone else will still be stalling for the right words.

What Voice-First Looks Like on a Messy Tuesday

So what does this actually look like in working practice? Let me walk you through a typical day with voice as my primary interface.

Most days, I'm not typing. I'm talking. Whether I'm writing a client email, drafting a strategy doc, or answering questions like this one, I start with my voice. Five minutes of voice dictation saves me 15 minutes of typing.

While ChatGPT's desktop app is often my starting point, I've found that limiting yourself to one platform is a mistake. Each system has different strengths for voice-first work:

• I feed transcripts to Claude when I need more creative, nuanced writing.

• Perplexity voice assistant has become my go-to for quick information—I've even tied it to a shortcut button on my phone for instant searches when I need facts or research on the fly.

• And MidJourney's conversation mode has transformed how I explore visual concepts.

The MidJourney tool in particular is like magic. You describe your vision, it generates an image, you reply with tweaks, and it iterates in real-time.

No prompting syntax. No parameters to juggle. Just pure creative dialogue that manifests visually. Since this launched, I've had hour-long sessions where I've built entire visual identity systems just by talking through concepts.

The key is experimenting across platforms to find which one handles different voice tasks best. Some are better for creative work, others for structured documents, others for quick answers.

From Thinking to Shipping: Three Beats

My writing flow has three beats now: speak, tweak, send. That applies to emails, memos, project specs, even long technical Slack replies. No more sentence-by-sentence overthinking. No more blinking cursor. Just velocity.

And the impact goes beyond personal efficiency. According to a 2024 Grammarly/Harris Poll, 73% of knowledge workers say generative AI (often voice-enabled) reduces miscommunication on teams. Voice interfaces aren't just changing how we work alone—they're transforming collaboration.

This workflow particularly thrives in async teams. I work across time zones, across tools, across contexts. Voice helps me clarify decisions, delegate tasks, and explain technical ideas fast without waiting for a call.

The technical barrier is nonexistent. You don't need a complex setup. Just a mic and a mindset. I use everything from a full desktop setup to just my iPhone and AirPods. Doesn't matter. The real tool is your willingness to talk before you overthink.

The Real Shift Is in Your Head, Not Your Tech

The most significant challenge isn't adopting the technology. It's changing how you think about "real work."

This is my new pencil. My new keyboard. I don't care what the purists say. Voice-first is a legitimate creative tool. Anyone still gatekeeping what "real writing" looks like is fighting yesterday's war.

Talking to the machine stopped being weird when I changed the audience. I just pretend I'm talking to a colleague. Or a friend. Or someone I care about. That's when it clicks. I'm not "dictating." I'm conversing.

This combination is powerful: Voice gives you the warmth. AI gives you the structure. A raw, emotional riff becomes a professional message. A scattered brainstorm becomes a strategic doc. It's not about control. It's about flow.

Async teams force you to be concise, but voice makes it human. I don't always get to talk to people in real time. But when I need to write something meaningful (a memo, a follow-up, a big idea), I want it to sound like me. Voice lets that through. AI makes it legible.

If adopting this approach feels weird, good. That means you're early. It felt weird to email, once. It felt weird to Zoom. Now those are just tools. Voice-first is the next one.

Once you’re comfortable with it, typing will feel like hiking with ankle weights. Voice will feel like a downhill run with the wind behind you.

Try This Voice Experiment Today

Let me give you a concrete way to test this for yourself, right now.

First, download the ChatGPT desktop app. Not the web version. The desktop one has native voice support that actually works. Open a new chat and select the little mic icon.

Side note: you’ve got two options here: voice dictation and the voice assistant. Dictation just transcribes your speech into text, which the model can then clean up or revise. The voice assistant, on the other hand, is a true two-way conversation—you talk, it talks back, and you can riff back and forth in real time.

I use dictation more often for drafting because it’s faster and more direct. But for long drives or deeper ideation, I lean on the assistant. Try both. Each has its own rhythm.

Next, pick one meaningful task you'd normally type. Not a throwaway reply. Choose something with weight: a strategic email, a technical explanation, or a memo that matters.

When you're ready, don't prep. Don't outline. Just talk. Hit record. Speak like the person's in the room. Say, "Hey, here's what I'm thinking," and just go. Forget prompting. Don't format in your head. Don't overthink the order. Let it be messy.

Once you've finished, get the output, but don't jump to edits. Hit the mic again. Say, "Can you revise that to emphasize [x] and make it more direct?" Let the model do pass two. Now you've got a clean, second-draft artifact.

All that's left? Paste, polish lightly, send. Done. You'll skip the slow-burn frustration of writing sentence by sentence. And you'll sound more like yourself because you are.

You'll know it worked when it feels weirdly easy. Like, "Why haven't I been doing this the whole time?" That feeling? That's your new workflow showing up. The best part is that this works even better on your phone.

🥡 Takeaways

🔹 The Creation/Consumption Flip. For the first time, voice interfaces enable creating high-quality content faster than consuming it. Five minutes of voice dictation saves 15 minutes of typing while delivering more authentic output. This isn't just efficiency—it's a fundamental shift in your relationship with creation.

🔹 Cognitive Advantage, Not Just Speed. Speaking activates different neural pathways than typing. Research shows this improves creative thinking, reduces mental effort, and enhances metacognition. Voice isn't just faster—it unlocks ideas that wouldn't emerge through typing alone.

🔹 Immediate Implementation Path. Start with ChatGPT's desktop app (not web), choose one meaningful task, and just talk without overthinking. Refine by voice command, then lightly polish. Experiment across platforms to find your ideal voice-first workflow.

⛺️ Ground Report

Let's look at the two signals I'm excited for this week—developments that are reshaping how we'll create in the months ahead.

🔗 Figma Makes Prototype Generation Its Next Move →

Figma just launched Figma Make: an AI tool that turns written descriptions or designs into working prototypes. This is a fundamental shift in who can build digital products. The gap between imagining and shipping is closing.

🔗 Apple and Anthropic's "Vibe-Coding" Partnership →

Apple and Anthropic are reportedly building a "vibe-coding" platform that writes, edits, and tests code from natural language. This signals app development becoming commoditized and increasingly disposable.

🌐 Open Channel: Heard on Substack

Last week, I shared a note that hit a nerve with the community:

This sparked some great conversations. It racked up dozens of replies and reactions, but more importantly—it resonated.

Shout out to , , , , , , , , , , and so many others who jumped in with sharp, honest reflections.

⚡️ Forward Signal

If voice interfaces are changing how we create, next week we’ll tackle a challenging step in AI adoption: avoiding costly tool mistakes.

I’ll break down the warning signs, hidden traps, and questions you need to ask before you commit to any AI solution—so you don’t get burned by hype or empty promises.

Voice changed my output. Spotting overhyped tools is what keeps me moving in the right direction.

Until then, try a voice experiment. See what changes when you stop typing and start thinking out loud.

P.S. If today’s insight shaved even five minutes off your mental load, please do me one quick favour—forward this note to a teammate who’s staring down the same AI storm. Every share gets us closer to 1,500 sharp-minded subscribers.

Share Frontier Notes for AI Leaders

AI moves daily now. On Monday I'm mastering Gemini's quirks, by Wednesday Claude throws a model curveball, and before the week ends OpenAI drops image capabilities that make all competitors obsolete (for now). The pace is relentless. The learning curve vertical. When acceleration becomes your baseline, even my tech-obsessed brain struggles to process it all. Overwhelming.

Have you noticed that weird stillness in your circles lately?

Despite everything changing around us, there's this collective pause happening. We all feel the tremors but hold our breath together.

You and I both know this isn't theoretical anymore.

Last week I read about companies that are already mapping "human-to-AI ratios" into their five-year planning. Another friend's startup replaced two workflows with early-stage agentic tools last month. The storm isn't coming—it's overhead. The first drops are hitting your windshield right now.

But here's what gives me hope: the fear is fading.

For the first time, I'm watching the skeptics in my network crack open the door. They're moving beyond their defensive postures. The questions have shifted from "Should we?" to "How can we?" That's where you and I come in.

Our job starts now.

Not watching the storm. Building in it. And this week's discoveries are perfect examples of both the opportunity and the challenge. I found a professor breaking down creativity in ways that might forever change how you use AI tools. I watched OpenAI's Codex turn a simple screenshot into a working app. But most importantly, I've identified what's actually killing your organization's AI adoption—and it's not what you think. Let's dig in.

⛺️ Ground Report

While everyone else doom-scrolls through AI predictions, I'm hunting for the rare signals that actually matter. These two discoveries aren't just interesting—they're transformative for anyone trying to navigate emerging tech:

A Stanford professor redefines AI creativity in 13 minutes flat.

I'm sending this video to literally everyone I know. It's that important. Unlike most academic takes on AI, this breaks down creativity in ways that permanently shift how you'll use these tools.

What makes it essential viewing isn't theoretical handwaving—it's the practical lesson for understanding where humans still lead, and where AI genuinely shines. Even my most tech-resistant friends are messaging me after watching: "Now I finally get it."

OpenAI's Codex turns a screenshot into working software.

While we debate AI's potential, the competition is brutal at the cutting edge. OpenAI quietly rolled out a Codex CLI to compete with Claude Code. In this demo, the OpenAI team use Codex to transform a simple screenshot of Apple’s Photo Booth into a functional app in seconds. No prompting gymnastics, no complex setup. Just upload, wait, and presto! This isn't incremental—it's a leap that eliminates entire stages of development.

🔎 Core Insight

Organizational Latency Is the Real AI Bottleneck

I'm watching a massive disconnect unfold in how organizations adopt AI. It's not about technology limitations. It's not about funding constraints. The real bottleneck? Organizational latency.

Nobody's talking about it. Yet it's killing momentum everywhere. And I can't stop seeing it.

The AI Speed Gap Nobody's Addressing

Here's the brutal truth: AI capabilities are evolving daily. Organizational adoption is moving quarterly. That gap isn't just unsustainable—it's widening into a chasm that's costing real money.

Information travels through organizations like molasses in January. By the time most teams hear about a tool, learn it, and implement it, the technology has already evolved twice over. This isn't a problem with monthly software updates. It's catastrophic when capabilities transform weekly.

Let me show you what latency looks like in practice:

Teams stuck days or weeks behind just learning new tools exist. Leaders haven't digested the information, let alone cascaded it downward. The firehose of AI advancements hits everyone simultaneously, with no map to navigate the chaos. In response, paralysis sets in.

This isn't one technology we're adapting to—it's a constellation. LLMs, agentic tools, fully autonomous systems, and looming AGI. All arriving concurrently, all demanding attention, none with an established playbook.

And here's the kicker: this delay isn't just frustrating—it's expensive. A 100-person company can waste approximately $1.7 million annually on organizational drag from slow decisions and processes. Scale that to 1,000 employees, and you're burning an estimated $17 million on pure inertia. Companies actively investing in new tech see 12% revenue growth, more than double the 5% growth of tech laggards. Every moment of hesitation carries a tangible cost.

The Proof Is in Past Transitions

A decade ago, I joined a dev team manually updating CSS styles across each of their 20+ websites (per client). I still remember watching them—talented people grinding through slow, error-prone workflows that somehow felt normal to everyone involved.

These developers weren't under-skilled—they were under-exposed. No one had shown them a better way. They weren't lacking talent—they were lacking a fresh perspective from outside their bubble.

When we implemented a smarter pipeline using basic build tools, SASS, and scalable architecture, deployment time dropped by half. The energy in the room changed overnight. Momentum followed clarity.

The lesson hit me like a truck: latency often masquerades as a knowledge gap, not a talent gap. People don't use better tools because they don't know they exist—or how they apply to their specific workflow.

Today's parallel is painfully obvious: there are brilliant developers who've never touched AI-powered coding assistants that could redefine their productivity. They're falling behind through no fault of their own.

And here's the kicker—organizational latency isn't just top-down. It's peer-to-peer. When individuals hoard breakthroughs or fail to translate them for colleagues, the entire organization slows to a crawl. One person moves fast; everyone else is stuck in molasses.

Why Even Sharp Leaders Miss This

I've watched even the brightest teams overlook latency—not from ignorance, but because today's pace of innovation is breaking all the old playbooks. The rules we built careers on just don't apply anymore.

The traditional implementation loop is too damn slow. Strategy → training → documentation → rollout made sense when tech evolved quarterly or annually. That cycle is now obsolete when tools transform weekly. I usually say it’s like bringing a sword to a laser gun fight.

Here's what happens: leaders default to what worked before—process. But over-documentation has become a liability. You'll spend more time designing systems for tools that will be outdated next week than actually using them.

The new game isn't perfect rollout—it's fast adaptation. Learning while doing beats pausing everything to build SOPs for a moving target. But this feels deeply wrong to veteran operators like us. We've been trained that precision equals professionalism. I get it. I've been there. I am there.

But in AI adoption, informality, curiosity, and shared discovery win every time. The teams that thrive aren't the ones with the most detailed playbooks—they're the ones with the most experimental cultures.

There's psychology at play here too. Research shows that ambiguous, high-stakes situations trigger decision paralysis among leaders. When facing uncertainty, executives can become bogged down by fear of failure, leading them to over-analyze rather than act. Anxiety literally activates the brain's threat centers, undermining rational decision-making. In practice, this means a well-laid strategy stalls out because leaders keep seeking more data or consensus, inadvertently freezing progress.

This is the reality we need to face: latency isn't a strategic failure—it's cultural. Teams aren't stuck because they lack a plan. They're stuck because there's no shared expectation that everyone explores, experiments, and passes knowledge forward. And that's on leadership.

Fear: The Hidden Drag Coefficient

Let's be honest about something uncomfortable: latency wears different disguises across the org chart, but it's almost always rooted in fear.

At the executive level: I've seen it firsthand—leaders who aren't hands-on with new tools simply don't know what's possible. That knowledge gap breeds indecision. Without a clear "Yes, we're an AI organization" stance from the top, teams remain frozen in wait mode. They're looking for permission that never explicitly comes.

At middle-management: This is where I spent years of my career, so I know the trap: mid-level leaders crave benchmarks, timelines, and approval. But waiting for perfect direction is just a stall tactic in disguise. What's more, their language shapes latency—leaders who speak about AI with fear or cynicism poison their team's mindset, even when they don't mean to.

At the frontline: Here's where it gets real: fear equals friction. People worry about automating themselves out of relevance. I've felt this myself. That anxiety breeds hesitancy, which kills experimentation exactly where it's most needed—at the coal face of daily work.

The bottom line? Latency is often fear in disguise—fear of being wrong, fear of change, fear of irrelevance. I get it. But inaction is no longer neutral. It's drag. And in today's environment, drag is death.

The good news? Studies show that leaders who manage these uncertainty stresses make decisions approximately 20% faster. Executives leveraging emotional intelligence and balancing data with intuition were 58% more effective in handling ambiguity. Overcoming the instinct to over-plan is a learnable skill.

What Low-Latency Organizations Actually Look Like

Let's be clear about something—low latency isn't a vibe or a feeling. It's a behavior you can observe, measure, and replicate.

Fast organizations talk about emerging tech constantly. In meetings, scrums, town halls, Slack threads—it's in the air you breathe. Leaders don't just mandate adoption; they speak publicly about their own experiences with new tools. Middle managers don't just relay instructions; they echo direction with enthusiasm. Nobody waits for permission—it's already implied in the culture.

I've worked with teams that don't just discuss merging tech—they use it daily in ways that quietly transform their work. Here are examples of how you you can use it daily now:

• ChatGPT for clearer writing and faster drafts

• Voice-to-text for capturing ideas on the move (a game changer)

• Coding assistants like Codex for development that would have taken 3x longer

• LLM feedback loops for quality control that used to require multiple human reviewers

Real-world examples prove this isn't theoretical. Take McKinsey & Co., hardly a scrappy startup. They built an internal generative AI assistant and achieved 70% adoption across 45,000 employees in record time. Their consultants now save roughly 30% of their time on research and document tasks. Or look at Replit, whose flat, experimentation-friendly culture allowed it to adopt AI coding tools rapidly, boosting developer productivity by over 30% almost immediately.

In these organizations, knowledge is shared, not hoarded. People who find efficiencies broadcast them like they've struck gold. There's a culture of showing your work—what you tried, what worked, what failed. No wait-and-see mindset exists because waiting means falling behind.

Here's your litmus test: if nothing feels different in your day-to-day work—even as the world shifts around you—you're in a high-latency organization. And you'll get outpaced by leaner, faster competitors who aren't dragging 2023 workflows into a 2025 world. Brutal, but true.

One Tactical Move to Start This Week

Want to kill latency? Here's your no-BS first step: Force the first move. Every manager—including you—must identify one AI tool relevant to their team and implement it this week. No budget excuses, no bureaucratic delay, no extensive planning sessions.

Make it a pilot—not a policy. This isn't about perfect long-term adoption. It's a short-term experiment with a tight feedback loop designed to shake people out of their wait-and-see mindset.

I've seen this work with simple examples anyone can try:

• Ops Leads automate one workflow with LLM-based chains

• Designers test image generation tools to speed up comps

• Project Managers use AI meeting agents for note-taking and action items

• Developers adopt AI coding assistants in the terminal (92% of programmers now use or are experimenting with AI code tools like GitHub Copilot)

The uptake isn't confined to tech teams either. Marketing and sales functions are early adopters of generative AI for content creation and personalization. Even traditionally cautious departments like HR report some of the largest efficiency gains from AI for recruiting and training tasks.

Here's the key: Blue-sky it. No rules. No SOPs. Don't hand your team a detailed guide—just the tool and a problem to solve. Let them figure out how to extract value. This creates ownership that documentation never will.

Then follow this dead-simple cadence:

• Week 1: Implement (just do it)

• Week 2: Review results (what worked, what didn't)

• Week 3: Iterate or discard (double down or move on)

This rhythm builds exploration into culture without stalling in endless planning. Playground over playbook. Stop waiting for the perfect top-down rollout that may never come. Start creating bottom-up momentum that's impossible to ignore.

The Remote Work Multiplier

If organizational latency is bad in co-located teams, it's exponentially worse in remote ones. I've built remote teams for years, and here's the reality: distributed work removes spontaneous knowledge flow. You can't tap someone's shoulder when you hit a wall with a new tool.

That silence becomes drag. Without clear venues for sharing, valuable insights get trapped—not from selfishness, but because there's no natural outlet. People solve problems alone, and their breakthroughs stay private by default.

As leaders, we must engineer the collisions that used to happen naturally. Create intentional spaces—weekly calls or async prompts—where people talk about what they're trying, what worked, and what didn't. Make this sharing expected, not exceptional.

I've learned that async beats sync for this kind of sharing. Not everyone speaks up in meetings. You need written channels (threads, pages, videos) to surface insights without interrupting flow. Let people contribute on their own time.

Most importantly: make sharing feel like leadership. When someone posts a breakthrough or AI trick, amplify it. Praise it publicly. Reference it later. Signal that this behavior is valued as much as hitting traditional metrics.

I've seen this work: the wildfire effect is real. It only takes a few visible experiments for a culture of experimentation to catch fire across teams—especially when distributed. One success story creates permission for ten more attempts.

The Choice Is Simple

Let me be blunt: the gap between AI capability and organizational adoption is widening daily. The companies that thrive won't be the ones with the biggest budgets or the most elaborate AI strategies on paper.

They'll be the ones that kill latency by turning experimentation into a cultural expectation rather than a special initiative.

The data makes this clear: organizations that rapidly adopt new technologies see 12% revenue growth, compared to just 5% for the laggards. In today's economy, that difference is existential. A UK study found that sluggish digital adoption cost their economy £111 billion in lost turnover. Every day you wait has a price tag.

I've been on both sides of this divide. I've seen what works and what stalls. The patterns are clear, and the choice you face as a leader is equally clear.

Start now. Or get left behind.

Takeaways (TL;DR)

• The speed mismatch is costing you real money. AI evolves daily, but your org moves quarterly. This gap isn't just frustrating—it's burning approximately $17K per employee annually in pure organizational drag.

• Latency is cultural, not strategic. Teams aren't stuck because they lack a plan—they're stuck because there's no shared expectation that everyone explores and passes knowledge forward.

• Fear = Friction. Executives fear making the wrong bet, middle managers crave perfect playbooks that don't exist, and frontline teams worry about automating themselves out of relevance.

• The Stakes: Start now or get left behind. Organizations rapidly adopting new technologies see 12% revenue growth, compared to just 5% for the laggards. In today's economy, that difference is existential.

🌐 Open Channel: Heard on X

Remember last week when we talked about ChatGPT's awkward personality update? Well, that didn't last long.

OpenAI's CEO admitted they messed up.

Sam Altman didn't waste time addressing the backlash. In a candid post, he acknowledged the recent GPT-4o updates made the model "too sycophant-y and annoying" despite other improvements.

The fixes? Already rolling out this week.

Meanwhile, users shared screenshots showcasing just how bizarre things got. One viral example showed ChatGPT responding to a deliberately ridiculous app idea with unfiltered contempt—a welcomed return to form.

What makes this noteworthy isn't just the mistake, but the speed of correction. From user complaints to CEO acknowledgment to fixes deployed—all within days.

Talk about an organization with minimal latency.

🧭 The Next Move

Here's your simple challenge this week: Pick one new AI tool. Implement it this week. Share one insight with your team. No excuses.

The time for perfect planning is gone. While you're crafting that strategy deck, your competitors are building muscle memory through actual implementation.

I’ll be back on Monday with the next newsletter. I'm diving into why audio might be your secret weapon in the AI era. Not even kidding—I've seen sound transform productivity in ways that feel like cheating. It will help you move faster. Text is so 2023. Trust me, you'll want to catch this one.

Lastly, an unsolicited music recommendation. I’ve been obsessed with Turnstile's new track "Seein' Stars" this week—check it!!

See ya Thursday!

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We've been here before. The pandemic forced an overnight shift that separated companies into two camps: those who adapted quick to hybrid and remote work and those who fought the inevitable. AI is creating the same dividing line today, just moving at 10x the speed.

In this week’s read, I'm tackling why the best teams create "playgrounds" for AI exploration before forcing rigid "playbooks." Smart leaders know that innovation requires breathing room first, then structure later. The teams that will win are the ones freestyling and experimenting now, not the ones bogged down in premature process.

Make sure you catch the notes at the end about the latest GPT-4o model update and why your AI responses might suddenly feel… different.

⛺️ Ground Report

Before we hit this week’s core insight, these are the two most important tech and business signals I’m seeing—the kind I’d tell my closest friends to pay attention to.

OpenAI’s Deep Research tool is flying under the radar… but it’s a game-changer.

Most users are sleeping on this: Deep Research quietly turns ChatGPT into a real research assistant, scanning and synthesizing across sources with serious depth. As lighter versions roll out to more users, this is one of the highest-leverage moves you can make right now.

Companies are already piloting AI as “team members.”

Microsoft’s Work Trend Index shows that forward-thinking orgs aren’t just dabbling—they’re experimenting with AI agents acting like project researchers. Teams form around a goal, then disband, like the Hollywood model. One emerging idea: companies will soon need a new metric—the human-to-agent ratio.

🔎 Core Insight

The Best Teams Build Playgrounds, Not Playbooks

Every tech leader faces the same challenge when building high-performing teams—especially as their organizations grow. And it's a challenge that's killing innovation at exactly the moment we need it most.

The problem is this: smart people tend to overcomplicate shit.

They don't just overcomplicate it for themselves. They build elaborate systems, documentation, and processes that weigh everyone down. It starts with good intentions—documenting "the right way" to do things. Before long, you've got 15 Notion pages detailing how to submit a report and a 37-step approval workflow for deploying a simple feature.

And at the end of the day, what happens? Innovation dies. Your AI implementation stalls. Your remote teams lose alignment. Your emerging leaders spend more time enforcing processes than solving actual problems. All while more nimble competitors run circles around you.

The Playbook Trap

Let's take a step back and examine why so many of us fall into this trap. There's this persistent myth that comprehensive documentation equals control. That if we just write down every possible scenario, we can eliminate risk and ensure consistent outcomes.

It's bullshit. And it's killing your ability to adapt in an AI-driven world.

Research consistently shows that rigid processes actually increase risk in fast-moving environments. At Netflix, they recognized years ago that process brings predictability, but also discourages creativity. That insight is why they've maintained their edge while competitors with binders full of procedures fell behind.

I've watched companies with thick policy manuals completely fail to deploy technology at scale, while more nimble competitors with simpler guardrails are already on their third generation of implementation. It's a game changer when you embrace principles over process.

The deeper issue is that playbooks fundamentally confuse alignment with uniformity. True alignment happens around principles and purpose. Uniformity is what you get when you force everyone to follow the same rigid steps regardless of context. One builds engagement; the other kills it.

Ever wonder why so many digital transformation initiatives fail despite meticulous planning? It's because they focus on process precision rather than getting genuine alignment around core goals. The playbook becomes the goal rather than the business outcomes it was supposed to enable.

What Makes a Playground Different?

Picture an actual playground for a moment. There's a defined space with clear boundaries. There are structures that enable certain kinds of play. There are basic rules for safety. But within those parameters, kids are free to invent games, take risks, create, and explore.

That's exactly what your team needs—especially when they're distributed across time zones and trying to integrate cutting-edge technology.

Apple doesn't get enough credit for this. For all the noise about Apple being a corporate giant, they deeply understand the power of human-centered sandbox environments.

Being part of the opening of a flagship Apple retail store years ago was a turning point for me. That experience reshaped how I view team culture and onboarding. The two-week orientation never once focused on products—no specs, no hard selling. Just people. That wasn't a bug—it was the point.

They prioritized connection and morale over sales training, policies or performance metrics. Apple let us discover our own play styles at work and gave space for that discovery to matter. Failure wasn't punished—it was part of the learning loop. You could be yourself. You could get it wrong. That's what built real team cohesion.

It's a lesson I carry forward to every team I lead or join: if you create a safe space for people to play like themselves, the performance will follow.

In practical terms, playgrounds are built on:

• Guiding principles over exhaustive rules. A handful of clear principles that everyone understands versus encyclopedic rulebooks nobody reads.

• Psychological safety over process compliance. An environment where people feel safe to suggest bold ideas and learn from failures.

• Distributed decision-making. Pushing authority to where the information is rather than forcing every decision up a rigid hierarchy.

• Lightweight frameworks. Simple iterative approaches rather than months of documentation and sign-offs.

Look at how Amazon operates with its famous two-pizza teams. Each small group functions like a mini-startup with end-to-end ownership of a discrete part of the business. They're guided by Amazon's Leadership Principles but have tremendous freedom in execution. The result? Faster innovation, better engagement, and massive scale without crippling bureaucracy.

For remote and hybrid teams, this approach is a no-brainer. When people aren't in the same physical space, forcing them to follow rigid playbooks creates more friction than alignment. Trust-based playgrounds let them solve problems in context while staying connected to the bigger purpose.

The Counterintuitive Truth About Constraints

Here's where it gets interesting: playgrounds still need boundaries.

If you're thinking, "Great, let's throw out all the rules and let everyone do whatever they want!"—pump the brakes. The data is clear: complete chaos is just as bad for innovation as rigid control. Teams need some structure to channel their creativity productively.

The counterintuitive insight is that certain constraints actually enable greater creativity. It's like how a sonnet's strict structure can produce more powerful poetry than free verse. Think about hackathons—by imposing a strict time limit, you force teams to focus and get creative with limited resources. LEGO's innovation team has a rule that any new product must be buildable from existing molds—a constraint that drives incredible creativity as designers find novel ways to use existing pieces.

The most innovative companies are simultaneously more tolerant of failure and more demanding of excellence. Gary Pisano from Harvard Business School describes it perfectly: "Creative cultures are paradoxical—they are fun and empowering, but also exacting in expectations." This is what separates truly innovative organizations from those that are just chaotically unproductive.

The Leadership Transition Challenge

This balance becomes especially critical for those making the leap from individual contributor to leader. I've watched this pattern repeat countless times: what got you promoted won't make you an effective leader.

High-performing ICs thrive by following processes meticulously. That's how they win. But leadership flips the script—it's no longer about executing steps; it's about designing the map. Most new leaders lack the tools to strategize. They know how to run plays, but not how to draw them up. That gap becomes painfully clear when they're asked to lead instead of do.

Push too hard on process? You kill morale. The team becomes robotic, allergic to autonomy, and slow to adapt. Push too hard on exploration? You get chaos. Everyone builds their own version of the same thing—and suddenly nothing aligns.

The balance isn't mechanical; it's emotional. Good leadership means sensing when your team needs structure versus when they need space. That requires emotional intelligence, not just SOPs.

And in moments of tech disruption—like the AI revolution we're experiencing right now—you need to bias toward playgrounds, not playbooks. Delay here turns proactive leadership into reactive cleanup.

Building Your Team's Playground

So how do you actually transform a process-heavy team into a principle-driven playground? I've seen this work across organizations of all sizes, and the pattern is clear:

1. Start with principles, not rules. Identify the handful of core principles that should guide decisions. Netflix does this with simple one-liners like "Act in Netflix's best interest" for travel expenses. These principles become the foundation that enables autonomy while maintaining alignment.

2. Create small, cross-functional teams. Break down silos by organizing around products or customer problems rather than functions. Give these teams end-to-end ownership and minimize dependencies that require bureaucratic handoffs. This is particularly crucial for remote teams who can't rely on hallway conversations to resolve cross-functional challenges.

3. Replace heavyweight processes with lightweight frameworks. Implement simple iterative cycles with regular checkpoints where teams can show progress and get feedback. For digital transformation initiatives, this might mean two-week sprints with demos rather than quarterly reviews with extensive documentation.

4. Make it safe to experiment (and fail). Actively celebrate projects that end in informative failure. Run blameless post-mortems focused on learning, not punishment. Tata Group even created "Dare to Try" awards specifically for bold attempts that didn't work out but generated valuable insights.

5. Engineer play for remote teams. Remote and hybrid teams lack the built-in playground of a physical office. You need to be intentional about creating space for exploration. This isn't about faking spontaneity—it's about scheduling structured freedom. Think: recess for adults. Time-boxed, purposeful, but loose enough for breakthroughs. Your team won't ask for a sandbox—they'll wait for instructions. You've got to build the playground and hand them the tools. Then give them the nudge to start building.

6. Regularly bust bureaucracy. Make it a habit to periodically review and eliminate unnecessary processes. Ask teams: "What's one thing we could stop doing tomorrow that nobody would miss?" I've never seen a team struggle to answer that question.

The results speak for themselves. Teams operating this way consistently show higher engagement, faster time to market, better quality, and more innovation than those drowning in process.

The key to making this work? Getting genuine buy-in from your team. Not superficial agreement—true, enthusiastic commitment to this way of working. When people feel ownership over the playground model rather than having it imposed on them, the transformation happens twice as fast.

Finding the Right Balance for Your Context

Let's be real—not everyone thrives in a completely free-form environment. When Zappos tried an extreme version of self-management (holacracy), about 14% of employees took buyouts and left because they preferred more structure.

The right balance will vary by team, function, and individual. Your AI research team probably needs more freedom than your finance team. Your experienced product developers might need less structure than your customer support team.

The art is creating what I call a "playground with a fence"—freedom within a framework of shared vision and values. You want enabling constraints (like game rules that make the game fun) rather than disabling bureaucracy.

For emerging leaders, finding this balance is probably the most challenging part of the transition from doing to leading. It requires constant recalibration as the team and market evolve. There's no static playbook for leading a playground—and that's precisely the point.

You'll know you've hit the sweet spot when:

• Ideas flow freely and get implemented quickly

• People spend more time creating than documenting

• Teams take initiative rather than waiting for permission

• Failures lead to learning rather than blame

• The organization can respond rapidly to new opportunities

Playgrounds in the AI Era

We're all living the AI experiment in real time. No one has the "perfect" case study yet—but the principles that separate winners from losers are already clear.

I've watched how differently teams approach AI implementation. The playbook-heavy teams get caught in approval cycles, risk assessments, and analysis paralysis. Meanwhile, playground teams are already on their third generation of implementation.

The secret isn't complex, but it runs counter to how many leaders instinctively approach new technology:

Start with permission and direction. Don't just suggest AI—authorize it. Tell your team: "Use these tools. Use them now. Use them often." Start with a short list, but expect that list to grow.

Structure the sandbox. Time must be carved out, not squeezed in. Give teams room on the calendar to tinker, integrate, and experiment with AI—no pressure for perfection.

Encourage bottom-up curiosity. After they play with your starter tools, ask what they want to try. Then explore those together. Ownership is everything.

Motivate through optimism, not fear. If your team thinks AI is a threat, they'll resist. Frame it truthfully as a massive opportunity to do more meaningful work—and mean it.

This isn't about automating humans out—it's about unlocking what they're here to build in the first place. The teams that approach AI with a playground mindset will outperform those waiting for the perfect playbook every single time.

The Final Word

Let me be clear: I'm not advocating for anarchy. I'm advocating for intelligent simplicity.

Whether you're leading a remote team through digital transformation, implementing AI tools, or transitioning from individual contributor to leader, the principles remain the same. The most effective teams have a handful of guiding principles, lightweight processes that enable rather than restrict, and a culture that balances freedom with accountability.

They solve the right problems because they're not bogged down in unnecessary complexity. They move faster because they're not waiting for approvals at every turn. They innovate because they have the psychological safety to try new approaches.

At the end of the day, this shift from playbooks to playgrounds isn't just nice to have—it's a killer competitive advantage in a world where change is the only constant.

Take a hard look at your team. Are you building a playbook that's suffocating creativity? Or are you creating a playground where people can do their best work?

The evidence is clear—playground cultures outperform playbook cultures every time. Set clear boundaries, establish a few key principles, then get out of the way. I guarantee you'll be surprised by what your team creates when they're not constantly checking the manual.

Takeaways (TL;DR)

• Rigid playbooks are killing innovation. Teams suffocate under complex playbooks and rigid processes, killing innovation exactly when it's needed most — especially for remote teams and AI implementation.

• Build playgrounds instead. Create spaces with clear boundaries and principles but maximum freedom within them. Structure enables creativity when it provides focus rather than control.

• Get real buy-in. The shift requires deliberate action: establish core principles, build cross-functional teams, celebrate informative failures, and get genuine buy-in. The reward? Faster innovation, better engagement, and a killer competitive advantage.

🌐 Open Channel: Heard on Reddit

Not every upgrade is progress—and the latest ChatGPT update is proof that faster doesn’t always mean smarter.

OpenAI just rolled out its GPT-4o model update—seemingly based on vibes alone.

Reddit users aren’t holding back.

Users want tools that respect their intelligence—not digital cheerleaders that respond to basic statements like they're profound revelations. AI hype machines are the new corporate yes-men, and ChatGPT's model update is the perfect example of rapidly changing tech going overboard.

🧭 The Next Move

We're now deep into the era of AI disruption—past the hype phase. This reminds me of mid-2020 when companies finally stopped debating if remote work was possible and started figuring out how to make it work:

• Companies are finally getting serious about AI integration

• The "explore and panic" phase is fading

• The focus is shifting to real tactics and long-term strategy

Look at Shopify—they're making waves because they started with a strong vision before jumping into tools. Meanwhile, too many organizations are still frozen in place—watching but not acting.

Even small steps matter right now. Creating playgrounds for your team costs almost nothing but builds massive momentum. And at the end of the day, alignment is more important than perfection. Get your team moving together, even if it's messy at first.

Quick gut-check before you go—

Thank you, thank you. I'll be back next week with more insights for navigating the AI REVOLUTION!

Until then, I’ll be at Friday’s Thunderbolts premiere — finally something from Marvel worth getting hyped about.

P.S. If today’s insight shaved even five minutes off your mental load, please do me one quick favour—forward this note to a teammate who’s staring down the same AI storm. Every share gets us closer to 1,500 sharp-minded subscribers.

Share Frontier Notes for AI Leaders

While everyone's chasing the latest AI announcements and debating hypothetical futures, the real story is happening behind closed doors. This is where leadership teams are making the decisions that will determine who thrives and who becomes irrelevant in the next 18 months.

The landscape is shifting faster than ever.

And the gap between organizations that commit to transformation and those that just talk about it grows wider each week.

The winners won't be the ones with the most advanced tech—they'll be the ones who fundamentally reimagine how work happens in a post-AI environment.

Last week, Shopify CEO Tobi Lütke posted a memo to his team about the company's new mandate for reflexive AI usage. This isn't just another corporate policy change—it's the digital literacy revolution of the '90s and 2000s happening again, except this time at warp speed.

The memo represents something I've been talking about for months: AI adoption isn't optional anymore, and organizations that treat it as such are creating their own obsolescence in real-time.

Before diving further, I'd recommend reading Lütke's original memo. It's only a page or two, but it frames everything we're about to discuss.

If you don't have time, here are the key points he outlined for his staff:

• AI proficiency is non-negotiable. Opt out at your own risk—stagnation equals failure in this environment.

• Prototype with AI first.

• AI skill development will be measured.

• Self-direct your learning, but share the wins and losses.

• Headcount requests require AI exploration first.

• No exceptions. Policy applies to everyone from new hires to the executive team.

What stands out about Shopify's mandate isn't how it compares to other companies' AI policies—it's that many organizations haven't even made it this far yet, at least publicly.

It's rare to see a company implement an overarching, company-wide AI policy that sets expectations for every role. Shopify makes it crystal clear: AI usage isn't optional—it's now a performance metric. They've tied AI usage directly to employee evaluation and even hiring practices. You need to be AI-aligned from day one.

That kind of clarity is precisely what's missing in most companies still in "wait and see" mode or looking for someone else to lead. They're trying to wield this new element without understanding its power.

By posting this internally and then sharing it publicly on his X account, Lütke didn't just issue guidance—he fired a shot across the bow of the entire business world. This is a deliberate stake in the ground, signaling that reflexive AI usage is the new standard.

Reflexive usage transforms tools into magic

Reflexive AI isn't about waiting to see how AI affects your role. It's about recognizing a fundamental shift in how we work.

Most people approach AI like they're trying to wield a magical element—attempting to shape and control it with perfect prompts and precise commands. They treat it as something external to be mastered through sheer technique.

But the early adopters understand something different.

AI isn't just a tool to wield—it's an arcane energy to channel.

Like a mage who no longer struggles with basic spells, the focus isn't on controlling every aspect but on developing a natural connection that lets you direct power effortlessly.

At first, you feel the resistance. Every interaction is deliberate and sometimes clumsy. But stick with it, and suddenly you're no longer thinking about how to form each spell—you're simply directing the flow, the technology becoming an extension of your intent.

Some developers have already recognized this feeling.

It's why you're seeing a new style of work emerging that the kids call "vibe coding"—where you're not meticulously crafting every line of code but instead having a collaborative conversation with AI about what you want to build.

The focus shifts from syntax to intention, from implementation details to the overall feel and function of what you're creating.

The people winning with AI aren't obsessing over perfect prompts or complete understanding. They're immersing themselves daily, developing an intuitive feel for the arcane current, and letting go of the need to control every aspect of the process.

This is how all technology transformations happen.

Remember learning keyboard shortcuts? That painful period of slowing down to look up commands eventually gives way to fingers that seem to know what to do without your brain's direct involvement.

This is the difference between struggling to conjure magic from your fingertips and becoming one with the arcane flow around you—between forcing energy into rigid forms and letting it naturally channel through you. The novice exhausts themselves trying to bend magic to their will; the adept simply becomes a conduit for power that was always there.

(Yes, I realize I just turned your business newsletter into a D&D campaign. Roll for initiative on whether you'll keep reading.)

We've seen this movie before: Same resistance, new tech

Magic metaphors aside, we've seen this movie before.

I always think back to primary school. My friends and I were often ahead of our teachers when it came to tech. The teachers had structure and frameworks, but we were the ones experimenting, moving fast, and figuring out how to harness this new element on our own.

I remember how controversial Wikipedia was—basically forbidden in classrooms. Teachers would panic when they saw us using it: "Anyone can edit that! It's not reliable!" Even Google got side-eyed. This moment with AI feels similar. Same resistance, same generational divide, same fear of losing control over how knowledge is created and shared.

You can feel the generational tension playing out in real time inside organizations. That said, it's not cleanly generational. I've seen incredibly creative older professionals adopt AI in thoughtful, effective ways—often, they're more open to the changing technology than some young professionals.

In general though, younger people are adopting their reflex faster, while older folks are ironically more equipped in some ways because they've seen paradigm shifts before. The experience of adapting through previous digital shifts actually primes you to use AI better—but only if you're willing to lean in.

Public AI stances aren't PR—they're battle lines being drawn

This is exactly what makes Shopify's move so interesting right now. While most companies are still navigating this generational divide internally, Lütke just cut through the noise with a public declaration.

This was 100% a signal to the market. Someone had to plant the flag, and Shopify did it.

It's not that other companies aren't bullish on AI, but this move felt premeditated—a deliberate stake in the ground saying, "Hey everyone, this is how we're proceeding, and here's what leadership in this space actually looks like."

Strategically, it's great PR. It positions Shopify as a forward-thinking company, which plays well with investors and the tech community. But it also sends a powerful message internally: "We're not hiding this behind closed doors. This is our direction, and we're proud of it."

It gives everyone in the company permission to go all-in on AI without feeling like they're stepping outside the lines or breaking some unwritten rule. They can now openly grasp this new element and learn to wield it without fear of overstepping boundaries.

The winners won't think about using AI—it'll be as automatic as typing

And that's where the real transformation begins. When permission becomes reflex.

Reflexive AI means every task starts with AI—without questioning if it's the right tool. It becomes automatic, like reaching for your phone when you need information.

I'm AI-reflexive now. Every communication refined through an LLM. Every presentation AI-edited before sharing. Every task enhanced by AI input. That's just how I think after over two years of daily LLM usage.

The reflex can manifest differently across roles, but the pattern is consistent—AI becomes the first step, not an afterthought.

This is what reflexive AI actually looks like in practice:

• Project managers automatically feed standup notes into Claude for blockers before updating sprint boards

• CFOs run reports through AI for anomaly detection before their first coffee of the day

• Sales managers have AI scan CRM notes each morning to flag opportunities

• Executives use ChatGPT voice to summarize board presentations while still in the car

• Team leads check meeting agendas for friction points through AI before sharing

• Designers generate mood boards and style explorations with Midjourney before starting visual concepts

• Developers check error messages with Copilot before manual debugging

• Content creators send transcripts to NotebookLM for themes before finishing recordings

These are just the obvious actions. This shift transforms work fundamentally, and those who wield the tools naturally will shape the future.

The most dangerous AI strategy? Having no strategy at all

While some organizations actively embrace AI like Shopify, the counterintuitive insight is that most aren't actively resisting—they're just silent.

And that silence is deadly.

If you're not in the conversation, you're unaware how fast things move—literally daily—putting you on a path to irrelevance. While others develop AI fluency and build new capabilities, silent organizations remain oblivious to the transformation happening around them.

If you don't know what ChatGPT can do, what image tools create, or how LLMs process data, you're already behind. It's not about being an expert—it's about having digital fluency to discuss what's possible.

Good leaders speak all languages—sports, pop culture, current events, literature—to see through others' eyes. The same applies with AI. You need to understand the tools even if you won't use them directly.

Teams will soon include AI agents as collaborators. Start speaking to AI like a colleague now using audio interfaces. Get comfortable and fluent, because it's inevitable you'll face this reality.

Remote teams already win the AI advantage

You can't have full team alignment in a remote environment without clearly outlining a shared focus.

And digital literacy is now a core part of that.

Being digitally literate today means understanding what tools are available, how to use them to your advantage, and when to pause expansion to see what's possible with just your own initiative and the right tools.

Remote teams are already closer to this shift. They interface with their screens exclusively and are often more exposed to AI tools in their remote meeting workflows. Tools like AI meeting assistants that summarize calls or generate outlines should already feel second nature to those teams. They're naturally more accustomed to wielding digital elements as extensions of their work.

These kinds of interactions should start to feel passive and automatic—just another part of the way we work. That's a big part of what's changing: the expectation that digital literacy isn't optional anymore. It's foundational to alignment in remote teams.

AI resistance exposes deeper team problems

When teams struggle with this new foundational requirement, it reveals issues that were likely there all along. Observing how groups respond to AI adoption mandates like Shopify's exposes something profound: resistance rarely stems from the technology itself.

Commitment is the big tell.

If your team isn't committed, it's not just an AI problem—it's a foundational team issue.

If your org's values don't include growth, experimentation, or a mindset around adoption, that's already a red flag. When AI adoption meets a wall of friction or fear, that's the moment to build a culture of exploration immediately.

This isn't optional anymore.

If people are describing AI as "scary" or "a nightmare" or saying "it's useless" or "I can do it better," those are signals they're stuck in the wrong narrative.

They're witnessing others wield this new element with increasing skill while remaining afraid to touch it themselves.

AI shouldn't be positioned as a job-taker. It should be seen as a tool to make life easier and help your team innovate at a higher level and push the standard of innovation.

And it starts at the top. If leadership is not all in on this, that is a massive warning sign. Your leadership has to lead the charge and show people how this will benefit the team.

Value-aligned AI adoption outperforms competition

This leadership commitment is what separates companies that strategically harness AI from those that merely experiment with it. While many organizations are still figuring out their stance, the market leaders are already moving with purpose.

Shopify planted the flag, but a few companies are already directing this new element with clear intention.

Salesforce recognized that friction kills adoption, so they've integrated AI seamlessly into existing processes. Their Einstein AI removes workflow friction with predictive analysis and automated workflows built directly into their products.

They prioritized accessibility over technical impressiveness, baking AI into their values and communications to achieve clarity.

Patagonia offers an unconventional example. Though not an AI company, they excel at technology-value alignment. They use tech to solve supply chain problems, not just for marketing.

They've created an ethical AI framework aligned with their environmental mission, using energy-efficient solutions that minimize impact. They implement AI selectively where it enhances core values and optimize supply chains to reduce waste.

You don't have to approach AI from one angle. Align it with your unique values, but be clear about those values and how AI fits your future.

No industry escapes the AI transformation

AI is going to touch every industry.

There is no escape hatch.

Many assumed creative fields would be last affected, but ironically, those were hit first. Writing, design, and video saw tools emerge hard and fast.

Now we're seeing AI move into technical spaces like development, with agentic AI on the horizon.

Over the next two years, the adoption curve is only steepening. When your grandmother uses ChatGPT, every industry should pay attention.

Even in labor-heavy fields like construction—where people think they have more time—combine AI with robotics, and that becomes a major innovation area. We'd rather break down robots than human bodies.

The bottom line: No industry can opt out of what's coming. This new element doesn't discriminate—it transforms everything it touches.

Build AI strategy from your core values

Since AI adoption is inevitable across all sectors, the question shifts from "if" to "how"—and the answer must be rooted in your organization's fundamental principles.

Leadership must have a candid conversation with no posturing.

Everyone should share how they're actually using AI now.

If someone isn't using AI at all, that's a red flag.

Once everyone's honest, shift toward values. How does AI integration align with what you stand for? I believe AI should be reflected directly in your organization's values—it's that significant. Determine how your company will direct this new element with purpose.

Communicate this clearly, strongly, repeatedly from the top down. Through all-hands meetings, manager discussions, and one-on-ones. Every single person needs to hear the same message: This is happening, get on the bus now.

The transformation is already here

Shopify's memo wasn't revolutionary because of its content—it was revolutionary because they said out loud what most tech leaders are still whispering behind closed doors.

The companies that will win aren't necessarily those with the most advanced AI implementations right this second. They're the ones creating environments where everyone is encouraged to experiment, fail upwards, and develop that reflexive relationship with AI as a natural extension of their work.

At the end of the day, this isn't about technical adoption—it's about cultural transformation. The companies that recognize this shift as fundamental rather than incremental will be the ones still standing when the dust settles. Not just through this wave, but whatever comes next.

The question isn't whether your organization will adopt AI. It's whether you'll lead the charge or be dragged along behind everyone else.

TL;DR

• Shopify's memo on "reflexive AI" isn't just corporate strategy—it's the declaration of a new standard where AI usage becomes as automatic as typing.

• At first, wielding AI feels awkward and deliberate, but with practice, it becomes an extension of your intent rather than a tool you consciously control.

• This isn't optional. Companies that treat AI as "nice to have" are already sliding toward irrelevance while their competitors develop new capabilities daily.

• The biggest red flag isn't failure to master AI—it's leadership that remains silent. If your executive team isn't openly discussing how they personally use AI, you're already behind.

• Remote teams have a slight head start—they're already accustomed to many digital interfaces as their primary work environment. The shift to AI-augmented workflows is a natural next step.

• This transformation exposes deeper organizational issues: resistance to AI often reveals problems with alignment, commitment, and values that were there all along.

• Start small but start today. The reflexive habit starts with deliberate practice. Choose one task this week where you'll use AI first, before your usual approach. Next week, add another.

That’s it, that’s all! The reflexive AI conversation is just beginning, and I'm curious to hear how your own teams are approaching this shift.

This is first part in my series exploring how fundamental changes in technology are forcing complete realignments in how we work. If this resonated, Wednesday's piece will challenge you about which leadership roles AI will impact first.

As always, I’d love to hear your own thoughts in the discussion.

P.S. If today’s insight shaved even five minutes off your mental load, please do me one quick favour—forward this note to a teammate who’s staring down the same AI storm. Every share gets us closer to 1,500 sharp-minded subscribers.

Share Frontier Notes for AI Leaders

Anyway, I try to save the really, really good stuff for Tuesdays when people still have their head in the game. So please enjoy this special edition where I talk about PROMPTS. Or rather, why you should probably stop obsessing over them and focus on something much more valuable instead...

I had a shower thought the other day about how most conversations around AI tools eventually circle back to "What prompt did you use?" or "How exactly did you do that?" People are frantically searching for the precise steps, convinced there's a manual they somehow missed.

Here's the thing: the killer outputs don't come from rigid formulas. They emerge from intuition and curiosity. It's not about following some perfect methodology—it's about playing in the sandbox like a kid. You create, explore, try stuff, see what sticks.

That experimental mindset is a game changer compared to memorizing prompting techniques or building a database of templates. Strip everything away, and success still comes down to the soft skills you already have (or should have): communicating clearly, asking smart questions, and expressing exactly what you want.

AI mirrors your clarity, not your technical prompting

A simple mental model for understanding this: think of AI as a mirror. Whatever you put in front of that mirror—your input, your intent, your clarity (or lack thereof)—is exactly what gets reflected back at you.

When your communication is intentional, thoughtful, and precise, the mirror gives you something actionable and useful. But when your input is vague, rushed, overly dense, or poorly articulated, you get a reflection of that same mess.

This model isn't complicated, but it's a no-brainer for understanding why communication skills matter more than prompt templates. AI doesn't magically add clarity—it reflects whatever clarity you bring. The clearer you are, the better the output. It's on you to bring your communication A-game and shape the response you want.

Four timeless communication principles that AI responds to best

This really boils down to fundamental communication skills—the same approach I'd use with anyone on my team. There are four core elements that consistently get better results than any prompt template:

1. State your goal. Start by clearly articulating what you actually want. Be explicit—what are you trying to achieve? Don't make the AI guess.

2. Provide context that matters. Briefly describe the relevant scenario. What industry? Who's the audience? What problem needs solving? AI needs this context to align with your intent, just like a human would.

3. Use precise language. Be specific and ditch vague, generalized phrasing. Don't assume the AI will magically infer the same things a human might—it often takes words literally (this is improving, but there's still a gap).

4. Structure your thinking. Break things down logically into steps, bullets, or sections. Structured input naturally leads to structured output.

These aren't prompt hacks or secret AI whispering techniques—they're basic professional communication skills. But they make a game-changing difference in how clearly and effectively AI responds to you.

Prompt templates fail when communication skills are missing

People are obsessing over the wrong things—fixating on specific prompts, exact wording, and templates instead of just communicating their intent clearly to LLMs like ChatGPT or Claude. They're treating prompting like some kind of standardized test with perfect answers rather than focusing on the actual goal: effective communication.

When someone asks me how I got a particular result from AI, they're almost always looking for the exact prompt—the magic incantation—rather than understanding the strategy behind it. It's like they believe there's a secret code that unlocks these tools.

This is a fact: the exact prompt doesn't matter. What drives results is good, clear communication. If you know your problem, can articulate your goal, provide relevant context, and outline the path forward, you'll naturally communicate effectively—whether you're talking to AI or humans.

AI tools are getting sophisticated enough that specific formulas and templates are going to be irrelevant soon anyway. But the communication skills that focus on clear thinking and articulating ideas? Those will serve you everywhere—with today's AI, tomorrow's technologies, and in every human interaction you have.

Collaboration trumps formulas in effective AI interaction

Believe it or not, there's an artistry to AI communication that most people miss completely. It's not a science—it's finding that sweet spot between giving too little context and drowning the system in unnecessary constraints.

Under-communicate your goals or leave out important context, and the AI isn't going to magically read your mind—you'll get garbage back. But the flip side is just as bad: overloading the AI with excessive technical details actually kills the creative process. You end up in this ridiculous loop of frustration, yelling at your chat bot, "Why didn't you do what I wanted?" (I’m one of the fools the robots will come for first.)

The magical approach? Treat the LLM like a creative partner or collaborator. You wouldn't corner a colleague at lunch and lecture them nonstop without letting them get a word in, right? (Right??) The same principle applies.

The interactions that produce the best results feel like natural conversations: clearly state your goal and context, then let the AI respond or ask its own clarifying questions. Trust is a game changer here—giving the LLM some room to interpret your intention often produces outputs that not only nail your vision but take it somewhere even better than you originally imagined.

Simple requests outperform complex templates every time

Smart people are massively overthinking this stuff, especially for basic tasks like reviewing emails. I've watched folks pull out these ridiculous prompt templates they found on a blog or LinkedIn that read like this:

I’m providing the email below for review, which I’ve drafted to send to a key client whose response could significantly impact our future business trajectory. Before you provide your feedback, carefully analyze the email for multiple dimensions of effectiveness: tone appropriateness (ensuring professional yet engaging communication), clarity and coherence (confirming it delivers a concise yet comprehensive message), persuasive strength… blah blah blah…

And it just keeps going with layers of unnecessary detail that nobody has time for.

All of that friction can be replaced with something straightforward:

Can you review this email before I send it to an important client? Check the tone, clarity, and effectiveness.

Done. That simple version gets you identical results without the copy-paste gymnastics from some prompt library. You're just wasting time dressing things up in fancy language when these systems already understand normal human communication.

A clear, direct request will always outperform a bloated, over-engineered prompt—in both effectiveness and efficiency. Just like in real life with actual humans.

Working with AI is about navigation, not memorization

Think of AI prompting like using a GPS—you input your destination and it helps guide you there. But just like with a GPS, you still need to know how to drive the damn car. You've got to steer, accelerate, brake, and make decisions at intersections—that's your fundamental communication skillset.

If you already know the city—your domain or context—you'll navigate even more effectively. Recognizing major landmarks and having a sense of direction means you're not slavishly following the GPS's every instruction for routine trips.

This applies directly to real-world scenarios: when MVPing a web app, you have two options for that first hour of work. You could spend it studying prompt engineering documentation, or you could use that time to craft a solid technical brief that clarifies your vision—not for the AI, but for yourself and your team.

The brief is the no-brainer choice because it has standalone value. It helps you define the project scope, clarify goals, identify users, and map out features. It's useful whether you're working with AI or human developers.

When I work with Claude, I simply feed it that brief and say, "We're starting a new project based on this brief," and I paste the whole thing in. That's it. The system gets to work because I've given it complete context upfront. No need to mess with complicated prompting or drip-feed constraints later.

People who focus on prompt engineering over project planning are doubling their time investment because they still need to create and input all that project context afterward anyway.

When AI reflects your communication shortcomings

I learned this lesson the hard way when I first used Claude Code to build a React app—specifically, a questionnaire for identifying weak points in business strategies.

I knew exactly what tech stack I wanted and gave Claude a simple directive to "build it in React," but I got lazy and didn't write a proper brief upfront. Because I wasn't specific enough about structure and standards, it built this bizarre Frankenstein app using a messy combination of vanilla JavaScript, JSX, and TSX files all mixed together.

Did it work? Yeah, technically. But it was a complete mess architecturally. If another developer looked at that codebase, they'd immediately ask, "What the hell is going on here, dude?"

This is exactly the kind of result people warn about with AI coding tools: spaghetti code with no architectural clarity, poor scalability, and zero consistency. But here's the thing—the AI wasn't the problem. I was.

I hadn't clearly communicated the full context or standards I wanted to follow. The vision was crystal clear in my head, but I failed to translate those details into the brief. I ended up having to go back and fix everything, creating unnecessary friction that I could have avoided by treating the AI like a proper collaborator from the start.

This taught me a crucial lesson: the failure point wasn't about needing a better prompt—it was about not providing clear enough communication upfront. There's no magical prompt template on earth that would have prevented that issue.

When AI exceeds your expectations

The flip side came when I gave Claude Code a comprehensive brief for a different project. This wasn't a prompt—it was a standard brief detailing technical requirements, user goals, timelines, and specifics you'd include for any real developer team.

Instead of passively waiting for instructions, it internalized the brief and took initiative. It set up appropriate languages and features, and—this is the killer part—proactively built in scalability and modularity because I had mentioned those were important in the brief.

I could immediately run the project locally. It had even handled the design work and included deployment instructions for Cloudflare Pages—something I never explicitly requested.

Watching it independently name functions and structure the application according to the strategic intentions I'd outlined felt genuinely human—like working with an insightful coding partner rather than a tool.

This completely shattered my assumption that AI interactions would remain transactional. It showed how deeply these systems can understand, interpret, and creatively extend human intent when given proper context. The difference between my two experiences wasn't about prompt engineering—it was about the quality of communication.

Seasoned leaders naturally excel with AI without prompt training

I've noticed something interesting: people who've been in leadership or management roles for years tend to naturally excel at AI prompting without any training whatsoever. Why? Because they've built the muscle of identifying root problems quickly, cutting through noise, and making fast, aligned decisions.

They already know how to frame a goal, clarify intent, and communicate with precision—all essential when working with AI. These folks don't waste time obsessing over the perfect semantic structure of a prompt. They just instinctively drive clarity and action.

On the other hand, people newer to leadership might still be developing those soft skills needed to communicate complex ideas simply. For them, prompt training might feel like a helpful shortcut.

But let's be real about the deeper question: what actually provides more long-term value for the individual and the organization? Learning temporary prompt engineering tricks that'll be outdated in six months, or developing timeless communication skills that transfer across every technology and human interaction?

The answer is a no-brainer.

The AI revolution flips the script on who prompts whom

We've already seen language models moving away from prompt-specific workflows—they're getting dramatically better at interpreting casual, natural communication. The future of AI interfaces isn't about perfect prompting; it's going to be multimodal: voice, text, images, gestures—whatever feels most natural to you.

Voice assistants are already demonstrating how these models can pick up on your tone, emotion, and even those little pauses and hesitations in natural conversation. The experience feels less like engineering a prompt and more like having an actual conversation.

We're also seeing image-based prompting get wild fast. Drop in a screenshot or photo, ask a question, and these systems understand the content in nuanced ways—not just reading data but grasping meaning, emotion, and abstraction. You don't even need to start with a question or direction anymore.

The most mind-bending shift? We're moving toward personalized AI assistants that learn your communication patterns. Eventually, they'll start prompting you with insights and suggestions. That's the big twist—while we’re discussing how to prompt AI, we're about to live in a world where the AI starts prompting us.

Given how fast this is all evolving, this article already feels late to the party. If you're still fixated on perfecting your prompt templates, you need to catch up. Things are moving at lightning pace and the game is already changing.

Prompt engineering dies while communication skills thrive

I see people getting weirdly obsessed with the idea that prompt engineering is going to be a legitimate profession or structured discipline. There's all this buzz about companies needing dedicated “prompt engineers” or “Directors of Prompting,” and maybe that'll materialize in some form.

But let's be real—the technology is evolving so ridiculously fast that most of this feels short-lived. We're racing toward a future where using these tools is basically like having a conversation, and I don't need specialized training to talk to a person—at least not as a functioning adult in a business environment.

Sure, we learn communication from childhood, but any adult should already have the foundational skills to communicate effectively without needing some kind of prompting playbook or template library.

As these models continue to improve at light speed, they'll need less and less technical prompting knowledge from us, and the value will shift dramatically toward just being a clear communicator.

At the end of the day, prompting as a distinct skill is already fading fast. The real differentiator moving forward won't be your collection of prompt templates—it'll be how well you can think, frame ideas, and collaborate with both humans and machines.

The future belongs to clear communicators, not template collectors—no matter what AI wave comes next.

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Over the past few months, we've had countless discussions about AI with our colleagues, friends, and strangers. I've noticed a pattern: people tend to mix up speed with true innovation. Some see AI as a magical productivity booster, while others view it as demotivating tech that diminishes human creativity.

What's been missing in these conversations is how AI can be a thought partner rather than just a shortcut. This piece has been baking in my mind for a while, and I'm excited to finally share it. It's a bit longer than usual, but if you're not quite sure how to leverage AI for true innovation or care about the future of creative work, I think you'll find it worth your time. I hope you enjoy.

AI tools have transformed our creative landscape overnight. Everyone's talking about how these tools make us faster—accelerating writing, coding, image creation, and just about everything else.

But here's the counterintuitive truth that's getting lost in the productivity hype: when everyone has access to the same AI tools, speed becomes the baseline, not the advantage.

The real question isn't "How can I use AI to do the same work faster?" It's "How can I use AI to create work that's fundamentally better?" This shift in mindset—from efficiency to quality—is what separates those who will thrive in the AI age from those who will merely survive it.

Think of your creative process like programming. For decades, coders have used a technique called "rubberducking"—explaining their code to a small rubber duck on their desk. It forces them to slow down, externalize their thinking, and catch bugs they'd otherwise miss. This simple act transforms problem-solving by creating a dialogue, even if it's with an inanimate object.

Now imagine that duck could talk back, ask questions, remember your previous work, and challenge your assumptions. That's what we have with AI in 2025—not just a time-saving tool, but a thought partner that can elevate your thinking rather than just accelerate it.

I'm seeing the opposite approach everywhere—the rush to use AI as a shortcut rather than a step up. Look at the book marketplace. There's a new wave of writers who aren't in it to create standout fiction or meaningful nonfiction. They're just using LLMs to crank out volume on a tight schedule.

It's flooding platforms like Kindle Publishing right now. Create a loose template, plug in some variables, and pump out what's essentially a mashup of greatest hits content. It's like R.L. Stine on steroids—and no offense to the legend himself—but minus the charm and intentionality of the Goosebumps books. Just churn out what the kids are calling "AI slop."

Same with image generation. Writers are ditching thoughtful, curated visuals and just running with the first prompt that pops into their head. The result? Bland, mismatched, and often nonsensical images that do nothing to support the writing or pull the reader in.

This low-effort, high-speed output is exactly what fuels criticism of AI. It creates noise, not value, and it distracts from the people using these tools with real purpose.

Quality over speed is the real AI advantage

I fell into the same trap at first. When I started using LLMs, I was immediately struck by the speed—writing went from taking hours to essentially seconds. Same goes for code and image generation.

It was easy to fall into the hype. AI would do the thinking, automate writing, and eliminate all the grunt work. Articles warning that copywriters are doomed and stories of people pumping out books weekly amplify this focus on speed.

But using these tools daily revealed the patterns and limitations. AI's tone, structure, and phrasing becomes very predictable. People are pointing out these stylistic patterns in others' work, even from professionals. We've realized that when everyone has access to the same toolset, the differentiation disappears.

And just like that, we’re back at square one.

I love that scene in Batman Begins where Commissioner Gordon tells Batman that if cops start wearing Kevlar, the bad guys move to armor-piercing rounds. And if Batman steps outside the law in a costume, he’s bound to provoke someone like the Joker to step out of the shadows. Escalation is inevitable.

Speed isn’t the edge anymore. It’s now the baseline.

So I decided to keep spending the same amount of time on my output, but now with an AI partner at my side. I didn't focus on just speed, and the result was much better quality and a far more enjoyable creative process.

AI removed all the tedious parts of writing and opened up more time for real thinking, refinement, and creativity. Instead of becoming lazy or derivative, this partnership has made my work sharper and my concepts more original.

I've heard this called "thought partner work," and I love that term. This isn't about generating AI slop as the naysayers say. It's about using the tool to go deeper, not to get there faster.

The bar isn't rising yet—but it will for those who think differently

When new technology enters the market, there's always a question of how it will change standards. Will it elevate what's possible or just make mediocrity more efficient? Looking across industries right now, I'm not seeing the bar for quality shift upward at all. It feels like it's either stagnated or dipped since AI tools became mainstream.

We're still in the early phase. What we're seeing is a massive shift in efficiency. People are getting their work done faster, moving through tasks quicker. Yeah, that opens up more time in the day.

But here's the monkey's paw: more time doesn't automatically mean better outcomes. For people taking the lazy path, quality stagnates and fulfillment tanks. They're moving faster but towards mediocre outcomes.

I don't know what the mental health data says, but I bet that kind of shortcut culture leads to burnout or creative disappointment. On the flip side, for people who look at these tools and say, "Hey, what if I use this to elevate my thinking?"—those are the ones who are going to thrive.

We're still at the beginning of this. Most people haven't wrapped their heads around how to use AI in a truly creative, elevating way. Everyone's talking about automation, but think deeper than that.

If something used to take four days to MVP, still take the four days, but now you've got room to iterate and refine it three times over. That's where the bar moves—not because of the tool, but because of what you choose to do with the time it gives you back.

The real problem isn't fear—it's indifference

When discussing resistance to AI, people tend to focus on fear: fear of job loss, fear of skills becoming obsolete, fear of a dehumanized future. This conversation has played out a million times in the last year, and I'm not pretending my answer is wildly different, but looking deeper reveals patterns that are crystal clear.

Look at the rise of machines during the agricultural era—farming jobs got replaced by machine jobs. The industrial revolution? Another massive shift. The cycle has always repeated itself. The automobile replaced the horse and carriage. Radio challenged the printing press. Then TV came for radio. Then the internet challenged TV. And now AI is coming for everything.

Look at the chart below. In 1800, over 75% of Americans worked in agriculture. Then machines came for the farms. By the early 1900s, manufacturing took over—factory jobs exploded as industrialization redefined work. That didn’t last forever either. By the 1950s, services started eating manufacturing’s lunch. Now? Over 80% of U.S. jobs are in services. Every era automates the last. And now AI is coming for the service economy fast. The cycle isn’t new. What’s new is the speed.

So yes, this is just the next iteration of the same cycle. Jobs lost, jobs created. Some people win big and others lose hard. And here's the thing—we can't just sweep that under the rug. We have to talk about it, not in a doom spiral, but in a way that acknowledges the pain while looking forward.

I hear people use words like "nightmare," "terrifying," "stealing," "the collapse of capitalism." And I get it. Hell, I use those same words all the time. But it worries me when smart people in my life can't even engage with this shift. Some are burying their heads in the sand, and it's going to hurt them long-term.

This change, this moment, will define our generation. There's no avoiding that. The best shot anyone has at coming through this well is to frame it positively, at least in the short term. Resistance won't protect you. Curiosity just might.

What's interesting is I don't think the most revealing fear is about AI itself. It's the lack of fear. What I'm noticing among people close to me—people I admire, respect, smart people—is not that they're afraid, but that they're not curious about this. They don't seem to fully grasp what's available in the market right now, let alone what's coming or the rate at which it's arriving.

That's the thing that worries me most. Not panic, not alarmism—just indifference.

From rubberducking to mechaducking: AI as your thought partner

Let me go deeper on this rubberducking concept I mentioned earlier. When programmers use this technique, the point isn't just to talk to a cute desk ornament—it's to slow down, externalize thinking, and get out of your own head. The duck helps you catch logical errors, rethink assumptions, and work through complexity. It's a stand-in for real collaboration when no one else is around or when it's not the right time to bug a colleague.

But now in 2025, we've got something that goes way beyond the rubber duck. These LLM tools aren't just passive listeners. They can talk back. They ask you questions. They can challenge your ideas. They remember what you're working on.

This isn't just rubberducking anymore. It's rubberducking on steroids. Actually, it's beyond that. It's rubberducking at a Godzilla-level scale. Maybe we should call this AI thought partner work "MechaDucking." (Remember who coined that!)

The collaboration is just on a whole different level now, and we should be taking full advantage of it.

I had a moment recently where this clicked hard for me. I was driving back from a work offsite about four hours away from home and wanted to explore a few ideas. So I plugged in ChatGPT to my car's Bluetooth audio, and I had a two-hour back-and-forth conversation while I drove. The only thing that prevented it from being a four-hour conversation was that I lost network connectivity.

When I got home, everything was there—a full log of conversation, a clean summary, the key points and outcomes, all ready to go on my desktop. It wasn't just some ramble I forgot—it was captured, usable ideation I could pick right back up on.

That's what this is enabling for us. Slow down, think out loud, and go deeper to draw insights; don’t just execute faster. It's incredible for learning, problem-solving, and even more powerful for communicating complex ideas clearly.

We've been doing this for hundreds of years—it's glorified journaling, but this time with instant feedback. Just like how rubberducking helped programmers debug code, mechaducking is going to help you debug your thinking.

Design your AI engine, don't just ride in it

The difference between casual AI users and power users isn't just about technical knowledge—it's about mindset. When you start looking at AI as a collaborative tool rather than just a utility, something clicks. You realize you can actually design a system that works specifically for you. I think of it like a vehicle, a machine, and you get to design the engine, not just ride in it.

Most people are stuck in prompt-and-reply mode, relying on default settings and basic interactions. That's surface level stuff. I’m not interested in the prompt game.

If you start at step zero, before the prompting, you can bake in your values, goals, and context into the engine itself.

For example, if you're using Claude, you can set up a project with a file directory that holds all the context for what you're working on. That becomes the engine powering that particular goal.

Let's say you have a public speaking project. What should go into that engine?

• A speaking style guide: a document outlining how you talk, how you want to sound, and what you want to express when you're on stage.

• Personal goals: what you want your audience to feel, what kind of speaker you're aiming to be.

• Practice speaking prompts that align with your tone and help you refine your delivery authentically.

The key here is self-awareness. You have to know yourself before you can design a system that reflects you. That might feel intimidating, but it's crucial. Any AI engine worth using should have a clear picture of your values, goals, background, and aspirations. That's what enables it to actually support you in a meaningful way, not just quickly generate fluff.

If you get this part right, what you build isn't just useful—it's powerful. It becomes a true partner in shaping the kind of output and creative work that reflects who you are.

There are definitely parts of my workflow that go against people's instincts. Starting with the willingness to share personal details with the system—that kind of vulnerability can feel risky, especially in a time where trust in tech companies has taken some serious hits. I get that. But I've moved past the apprehension because the value is just too high, and honestly, it's been worth it.

The other big shift is how I've let go of conventional expectations around what a writing process should look like. We're conditioned from an early age to follow this formula: brainstorm, then notes, then draft, then revise, then feedback, then publish. That process is seen as sacred. It's even used to measure aptitude in our academic systems.

So when I say I use a mix of audio techniques, AI brainstorming sessions, transcription writing, and interview-style exploration, it throws people off. But here's the truth: I'm spending more time generating ideas now. I'm going deeper. I'm putting out more authentic, valuable, and intentional work.

That's the bar I'm setting for myself, and if people want to judge the process rather than the result, then that's on them.

Just try it—you have nothing to lose

One of the biggest barriers to embracing new technology is simply getting started. The gap between not using AI and using it effectively can seem enormous. But here's my advice if you're resistant to trying these tools in a new way: just try it. Seriously, just try it. You have nothing to lose.

Take something you've already made—a piece of writing, a sketch, a photo, something that was never intended to be used with AI tools—and just feed it in. Everyone's got something. Might be a note on your phone, a doodle in your sketchbook, a random photo on your camera roll.

Use that, then ask the system to change it. That's it.

Maybe it's a photo of your house and you tell it to remove the power lines. That's totally practical. Or maybe it's a sketch you made—take a picture, upload it, and ask the AI to generate a professional-looking image based on the sketch.

You'll be surprised. It keeps the essence of your original work but shows you something new, something you didn't intend for it to do. And that's the point. Now you can keep reiterating on that until you get something that really resonates.

It's not about making something to publish or show off. It's not about selling it or adding it to your portfolio. It's just about unlocking what's possible. A pure creative nudge, and the barrier is so tiny. This takes less than five minutes, assuming you have access to a basic tool.

And even if the feature is behind a paywall, what are we talking about here? $15 for a month? That's the cost of lunch. You've got nothing to lose, but you might just come away with a completely different perspective on what creation can feel like.

The 80/20 rule of AI creativity: Make the last 20% all you

The biggest concern people express about using AI in creative work is losing their authentic voice. It's a legitimate worry—how do you maintain originality when working with tools trained on everyone else's output? The answer isn't complicated, but it requires self-awareness.

Like I've said before, this all comes down to knowing yourself. If you want your voice to stay central in any AI collaboration, you need to be clear on what you value, where you're going, and what you actually want to create.

I'll admit this might be an unfair advantage on my part, but I've always lived a value-led life. Even before using AI tools seriously, I always had that clarity. I have a personal document that serves as kind of an operating manual for me. It includes a few lines about who I am, my five core personal values, my life goals, my ten-year vision, my priorities and how I weigh them, and quotes and mantras that resonate and guide me.

That document is my baseline. I use it to align every creative project I touch, and when I work with AI, I share that with the system.

Doing that makes a massive difference. It helps reduce the generic tone in the responses and gives the model something to align with that's rooted in my worldview. It doesn't eliminate bias or blandness entirely, but it gets me a hell of a lot closer.

And from there, it's on me. I take what comes back and I revise it until it feels true. That's the whole game. Use the machine to get 80% of the way there, but make sure the last 20% is all you. If it's not aligned to what's in your heart, it's not ready to put out into the world.

That criticism that using AI somehow diminishes creative ownership? Honestly, that criticism is boring at its core. It often comes from people who ironically aren't thinking that creatively to begin with. They're still treating AI like a Google search—"Make me an ad in the style of Apple." The system spits something out, it gets copied, pasted into a LinkedIn post, and people call it their work.

That's not creativity. That's a command. There's no iteration, no refinement, no authorship, just a one-click stunt.

So in that sense, yeah, maybe AI does diminish creative ownership for people who lack imagination. But for the rest of us—for the ones who actually want to mold ideas and explore unfamiliar angles and build something original—it's a multiplier.

The key is to use your imagination, build your own workflows. Don't just follow the obvious path. Invert them. Look at what everyone else is doing and then ask, "What's not being done? What's missing? What can't AI do in this environment, and how do I lean into that?" That's how you own the process. That's how you push past average and create something only you could have made.

The bar needs to be raised, period. And if you're serious about ownership, then own it. Don't settle for being impressed by the first output. Go deeper than that.

AI removed my creative friction, not my creative work

Executive function—our brain's ability to plan, focus, remember instructions, and juggle multiple tasks—is often the hidden barrier between ideas and execution. For creative professionals, limitations here can be particularly frustrating when you have the vision but struggle with the process.

The biggest shift for me has been consistency. I've always been good at starting projects, but keeping that energy going was another story. Not because I lacked motivation or wasn't willing to work hard, but because of the friction—the tedious, repetitive, low-value tasks that pile up and drain your cognitive energy. That's what would slow me down or derail momentum.

And sure, some people would say, "Well, that's just the grind. That's the hard work." And yeah, maybe. But we're past that now.

AI removes those friction points. It clears the runway. The low-level functions—sorting ideas, formatting, transcription, reorganizing messy thoughts—those are no longer things I have to spend mental energy on. Now I get to stay in the zone where the value is the highest: strategy, creativity, ideation, synthesis. The fluff is handled, and what's left is pure innovation and exploration.

And here's the thing—I think a lot of people want to put in the hard work, but they want to put it into something that matters, into high-value work. Historically, they just haven't had the support. No team, no assistant, no scaffolding. AI levels that playing field. That support system is now available to anyone who wants to pick it up and run with it.

This might sound cocky the way I put it, but I've always been capable of the work I'm doing now. What's changed is that AI has allowed me to do it better.

Here's the key: I refuse to let efficiency and speed dilute the value of my work. I've always been motivated. I've always had the drive. But friction, tedious steps, second-guessing, mental clutter—it slowed me down. Now that friction is gone, and what's replaced it is a consistent flow that leads to high-level output.

The process is so much fun now that I actually can't stop. There's no hesitation, no resistance, no doubt that I can follow through. That's really what this comes down to. Self-doubt used to hold me back from completing bigger, deeper, more vulnerable work. What AI changed wasn't my skillset—it was my confidence.

Specifically, it's opened the door for long-form writing projects that I'd either start and abandon or never start at all. But even more than that, it helped me start putting my thoughts out into public. That's been a huge shift—just getting the ideas out there confidently without overthinking or holding back.

And in smaller ways, it's even helped me communicate better with people who I don't know very well. It's sharpened how I write, how I frame things, how I express myself, especially in those early conversations where the trust hasn't been built yet.

The tools didn't give me ability. They helped me use it more fully and share it more openly.

Saturday morning miracles: what happens when AI unlocks your creativity

There's a profound shift that happens when AI tools remove the technical barriers that once limited your creative expression. Suddenly, ideas that seemed impossibly complex become accessible weekend projects. The scope of what you can create expands dramatically.

The biggest shift for me has been my willingness to play in the sandbox. I hadn't written code in about five years. I moved into leadership, and over time stepping back into dev work felt completely intimidating.

If you've done any development before, you know how fast it moves and how easy it is to feel like you've lost your edge. But once I started using AI tools, I realized I still had the foundational knowledge there, but now I had support. I could use tools like Claude Code to spin up fast MVPs using new engines without the friction or hesitation.

That was a huge unlock for me. It brought back a sense of agency, the ability to just make things for the sake of making them. What used to be a four-weekend hobby over a month now started with a Saturday afternoon exploration. That's how much the process has changed.

Honestly, I feel like a teenager again, or like I'm in my early twenties just building stuff on the weekend for the joy of it. Now I have the time. I carve out the time, and I'm creating things I'm genuinely proud of on the day-to-day.

It's also changed how I approach work. I'm more open to weaving creative ideation into serious projects because the tools lower the barrier and raise the impact. It's made my whole life, not just my workflow, more effective.

An easy example for me is video. I'm not a filmmaker and I never intended to be. I love film, but the process never really called to me. But I do see the value of motion when it comes to expressing or pitching a concept.

As an experiment, I recently took a poem I had written—a piece that lived quietly in my notes app for months—and turned it into something completely unexpected. First, I fed it into an image generator to create visuals based on the poem. I used Midjourney to create stills that match the tone and symbolism.

Then I used Luma to animate those stills into 30-second video clips. The consistency and style made the transitions feel intentional and cinematic. That alone was powerful, but it didn't stop there.

Originally, I was going to overlay a voiceover for the poem, but I realized I could go further. I used a songwriting engine (Suno) to turn the poem into a Broadway-style musical track inspired by the musicals I loved as a kid like Notre Dame de Paris. I layered the music over the animated visuals.

The final result? Something emotional, something haunting, and honestly, something I never planned to make came together in a single Saturday morning. Pure exploration. No goal, no pitch. I shared it with close friends and got amazing feedback. People asked, "What are you doing with this?" And the answer was simple: nothing. The whole point was to make something beautiful and no-stakes.

What I walked away with was a better understanding of what's possible right now with AI and creativity and how to bring together different threads in my life. The writing came from the heart. The music reflected my childhood influences. The visual direction was something I'd always seen in my mind but never had the tools to create.

And now I know when the time comes to do something bigger with this kind of technology, I'm ready.

Corporations demand ROI while individuals build intuition

The gap between how organizations approach AI and how individuals do reveals a fundamental disconnect in innovation. Large companies create policies before understanding capabilities, while individuals discover capabilities that later inform policy.

I think it really comes down to empowerment. Individuals are figuring this out by testing, playing, and pushing limits. That's where the insights are coming from, not from top-down policies.

Corporate environments tend to demand clear outcomes. Everything has to be measurable. Everything has to be justified. But right now, we're in a phase where play is critical. People need space to experiment in order to understand what these tools are even capable of. Without that freedom, teams won't be able to build intuition, and without intuition, they can't innovate.

Frankly, I don't see major corporations outpacing small teams of two or three who are embracing this stuff right now. The shift is too big. It's happening too fast. Agility is the advantage right now, and the individuals and micro teams who lean into this early will out-create the legacy players who are still stuck asking for ROI on every experiment.

And this is a core theme of everything we've talked about here. Stop treating AI like it's just about time-saving or boosting productivity. Yes, it will make things faster. But once everyone is using these tools, we're all back at square one.

If speed is your only metric, you're not gaining a competitive edge—you're just keeping up. How boring. How unfulfilling. The real opportunity is to push beyond that, use the same amount of time you have and take the efficiency gains and reinvest them into the creative process. Make the work better, not just faster. That's where the value is. That's how you stay ahead.

The organizations that will win are the ones that give their people room to explore and push boundaries, who prioritize novelty, strategy, and authentic output over shallow gains. Innovation isn't about getting to done faster. It's about creating something truly new and valuable.

Be a weapon, not just another employee

So where does all this leave the individual trying to navigate between what their organization allows and what they know is possible? How do you balance institutional expectations with personal effectiveness?

I'd say just learn how to play. Use all the extra time in the margins. Go live your life, get outside, touch grass. Spend time with your family and friends.

But when you have those little pockets of time, when you usually fire up a new game or binge a new series, open up a new sandbox instead. Instead of picking up the new Assassin's Creed this weekend, spend some time learning the quirks of these new tools.

Build curiosity and familiarity on your own terms outside of institutional context. Don't be afraid and cynical about these tools as they enter the marketplace. Everyone is throwing their opinion out into the noisy internet, but take it upon yourself to create in the face of that.

And then when you bring that mindset into your work, you're not just another employee. You are a weapon. You've built intuition, you've experimented. You're not just waiting for permission or policy changes. You're already innovating.

And that's going to be extremely valuable in the environments we're all heading into.

When everyone has AI, better wins—not faster.

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Whew, that was a dense read… If you found this valuable, I'd love for you to support my little newsletter. Frontier Notes explores the intersection of technology, leadership, and the future of work without the usual hype or doom-spiraling. Just practical insights from someone in the trenches.

Thanks for spending part of your Saturday with me and my thoughts.

P.S. If today’s insight shaved even five minutes off your mental load, please do me one quick favour—forward this note to a teammate who’s staring down the same AI storm. Every share gets us closer to 1,500 sharp-minded subscribers.

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"Asking the wrong question is more dangerous than giving the wrong data."

I appreciated the insight. Look at the paradox we're facing in 2025: we have access to more information than ever, yet our teams understand core problems less clearly than before.

The rapid proliferation of AI tools bombarding us daily creates even more data to sort through. Teams are drowning in notifications, updates, and new features while missing what actually matters. And if you're part of a distributed team, you're likely facing deeper challenges that go beyond just which Slack channel to use.

To top it off, our wacky economic environment creates unspoken pressure to deliver results quickly without taking time to properly understand what's happening. All of this leads to teams obsessing over details while the real problems float by unaddressed.

Smart leaders drown in details that don't matter

I've had my healthy share of sticky situations where I was presented with a challenge and overcomplicated the solution. It's so easy to treat everything that crosses your desk as a mission-critical, all-hands-on-deck issue.

Many of my biggest fumbles have come from being asked to create proposals for people and not truly listening to what they were asking for. I'd over-engineer solutions that were out of budget and didn't solve their actual issues. When we're not listening to what people are asking for, we get involved in tasks that don't contribute to a happy outcome.

The most common reason leaders get trapped in the details is because they get lost in the day-to-day. They have a stack of work they're responsible for and move from task to task to task.

What ends up happening? Things get delegated, shuffled, and then explained away passively without actually trying to understand what someone needs. The details of the ask get used as an excuse to inquire more, gather more information, set up more calls, and include more people.

I don't think anyone who does this is doing it intentionally—but it creates a bigger issue than ever needed to be.

This challenge gets amplified in a distributed environment. If you pose a question to the void of your communication platform, it's easy to assume all eyes are on the problem. But on the other side, team members can easily ignore it, assuming someone else is already working on a resolution.

In remote setups, people often hide behind digital silence, leading to passive accountability where whoever is available focuses on the solution instead of the right person. The scattered data across platforms, cloud drives, and communication threads makes it worse—collaborators might not even know if they have access to the data or if it exists at all.

Your real problems are dragons hiding beneath broken bridges

Let me offer a metaphor that's helped me think about this challenge.

Problem solving is like building a bridge. Most teams start by analyzing what's broken on their side of the ravine—the broken planks, the weathered rope, the unstable foundation. That approach seems logical, but it's backward.

What if instead, we first envisioned the complete bridge we're trying to build? What does the ideal outcome look like when you reach the other side?

When you take this approach, something interesting happens. You start to see the real obstacle isn't the broken pieces of your current bridge. There's a dragon in the ravine—a fundamental challenge that's causing all those symptoms you've been obsessing over.

This dragon represents the real, often unspoken problem—the deeper issue causing recurring symptoms. Most teams get distracted analyzing every tree and rock along the path instead of confronting the dragon directly.

Inversion is the counterintuitive approach most leaders miss

Here's a simple mental model I use to spot the dragon: the inversion method. I first heard about this from Charlie Munger, Warren Buffett's partner and a lover of mental models.

This approach involves thinking about the opposite of what you want to achieve and focusing on how you can avoid failure rather than solely on how to achieve success.

For example, a client might keep saying "the interface isn't working for our users," and the team goes into endless tweaks. But the real problem could be that the client never defined who their actual target users are or what outcomes they need from the tool. Both sides get stuck in a cycle of minor fixes.

We can simplify by asking the client to clearly describe what a flawless outcome (or total failure, depending on scenario) would look like. Then we work backward from that vision to identify key conditions, processes, and gaps we need to bridge. This allows us to quickly compare ideal requirements to the current state and determine the root issue preventing the client from achieving their outcome.

Others might say inversion neglects more nuanced, incremental improvements needed in real-world contexts. But it's useful for much of the day-to-day challenges that many young managers face.

And remember, not every team member or client is comfortable articulating an ideal vision. That's where leadership comes in—bringing in someone who can visualize that outcome when it's required.

Diving deep often means you're diving in the wrong direction

When we dive too deep into details, we risk analysis paralysis—spending more time dissecting the problem than solving it. Our instinct when presented with a hard question is to roll up our sleeves, delegate responsibility, create tickets, block out time, and book calls.

But sometimes a problem is actually just a question disguised as a problem.

Ask the person who brings up the issue if they understand why it's a problem. It seems simple, but I've observed this missing step for years. Put yourself in your peers' or clients' shoes and try to identify if there's an understanding gap that's causing them to shape a question into a problem.

One trap to avoid is the relentless pursuit of drilling down into every detail until you uncover the deepest root cause. There's a time and place for that, but effective leaders need to know when to step back, make a decision, and communicate priorities. This is where trusting both data and your personal intuition comes into play.

Some details change everything while others waste your time

Not all details are created equal.

The details that matter:

• Directly impact primary objectives (like a consistent drop in user conversion rates that reduces revenue)

• Link to fundamental issues

• Recur consistently over time (persistent software bugs affecting significant users on every update)

• Are supported by reliable data

• Are critical for stakeholder success

The details that distract:

• Peripheral symptoms with minimal impact (occasional typos in marketing emails that don't affect campaign performance)

• One-off incidents (rare system errors during unusual traffic spikes)

• Lack clear data support (anecdotal comments not reflected in broader feedback)

• Are irrelevant to long-term goals (social buzz that doesn't translate to improved business outcomes)

• Result from misaligned focus (chasing vanity metrics like temporary increases in page views that don't impact revenue)

At the end of the day, if you're not focused on revenue goals, you'll waste a lot of time. As Winston Churchill wisely noted, "You will never reach your destination if you stop and throw stones at every dog that barks."

AI tools make spotting the dragon easier than ever before

Luckily, we're living in a time with new tools that can help us spot that dragon lurking beneath our bridges.

LLMs like ChatGPT or Claude are killer at analyzing situations and presenting approaches. Since we know which mental models work and what we're trying to identify, a simple prompt can help:

I am facing [this problem]. Here is the current situation and key symptoms: [outline the situation]. Ask a series of 'why' questions to help me identify the underlying causes.

The LLM will provide insights you can use to decide how to proceed. You can even ask it to assess whether your solution will fix the problem—not to take at face value, but as an extra confidence boost combined with your intuition.

This is a no-brainer right now. We're in a gold rush in terms of tool availability and pricing. There's never been an easier time to communicate clearly to teams using these AI assistants.

Missing the real problem costs more than any solution

When we fixate on the wrong problems, the consequences ripple throughout the organization:

• We invest time and resources that yield no strategic value

• Confusion and internal friction develop

• Core issues remain unaddressed, undermining innovation

• Trust erodes in both your team and with clients

• The entire organization can lose focus, resulting in diminished competitive advantage and falling revenue

Leaders should openly share their own uncertainties or mistakes during discussions about finding root problems. Often challenging problems escalate up the command ladder, but even as leaders, we don't always have the answer.

This is where emotional intelligence matters.

When a problem lands in your lap, let everyone know "we'll figure it out" without losing your cool. Always return to the basics: define the ideal outcome, assess the current state, identify gaps blocking success, and then be decisive and communicate immediate actions.

If you find yourself the de facto problem solver, remember to delegate this process to others. It's important for the team to learn and practice these skills—give a man a fish or teach him how to fish, as the saying goes.

A stolen donut reveals why we miss what actually matters

Let me close with a personal story about how focusing on the wrong issues can have a lasting negative impact.

When I was in primary school, my classmate Hope won a competition and received a donut from a local bakery. If you grew up in the '80s, you know local bakeries went hard, and that donut might as well have been a bar of gold to us eight-year-olds.

Hope kept the prized donut in a decorated box on her desk. Our class left for library time, while Jack, another classmate, stayed behind in detention for acting out that morning.

When we returned an hour later, the donut had disappeared. Our teacher singled out Jack, who had chocolate smeared on his face, and verbally berated him for stealing in front of everyone.

This story became legendary among Jack and our peer group. But years later, talking with a former classmate, we had a sad realization: Jack didn't steal the donut out of spite or malice. In hindsight, knowing what we learned about Jack's home life, he was hungry.

Our teacher didn't display leadership. Instead, she humiliated Jack and failed to identify the root problem. She missed an opportunity to get Hope another donut without embarrassing Jack, and perhaps get him some healthy food.

It's not a feel-good story, but it perfectly illustrates what happens when we focus on symptoms (a stolen donut) rather than root causes (a hungry child).

Building past the dragon requires seeing the complete bridge

I think over the next few years, teams might become too dependent on tools, letting machines resolve too many of their problems. When truly unresolvable challenges arise, it will be more important than ever to return to simple mental models and be willing to take decisive action.

Remember the bridge and the dragon. Start by defining your ideal outcome on the other side of the ravine. Compare that vision with your current reality to identify what's missing. Spot the dragon—the real problem causing recurring symptoms. Then be brave enough to confront the dragon directly and begin building with strategy and intent.

Because at the end of the day, asking the wrong question is more dangerous than giving the wrong data.

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When organizations pitch new technology based on excitement rather than utility, the pattern is painfully predictable: high initial interest followed by rapid drop-off, low team engagement rates, a bloated technical stack, and eventually, increased internal frustration and attrition. I've seen it happen time and again.

Technology adoption is like tending a garden. When you lead with hype instead of practical benefits, you're essentially scattering seeds on unprepared soil—they might sprout with initial enthusiasm, but they won't take root or thrive.

Here's the thing: selling on hype instead of practical benefits is the fastest way to ensure your technology adoption fails. When we lead with "this is exciting" rather than "this solves your problem," we're setting ourselves up for disappointment.

The metaverse is a perfect example. For about a year, businesses were scrambling to establish a presence without asking a fundamental question: what actual problem does this solve for us? The hardware infrastructure wasn't ready (and may never deliver what we imagined), but companies jumped in headfirst anyway, driven by fear of missing out rather than identifying a practical use case.

Look, there's a lot of fear in the workplace right now. With agentic AI and a wave of new tools hitting the market, people are legitimately worried about what this means for their jobs. When Bill Gates says doctors and teachers will be replaced within a decade, it's hard not to feel that anxiety ripple through every industry.

The reality is we're seeing exponential growth in AI capabilities, but humans are slow to adapt. There's a fascinating contradiction happening: on one hand, the technology is advancing at a frightening pace, yet on the other, I'm still meeting people who are just waking up to the fact that ChatGPT exists. Some of my relatives barely know what it is, while these tools have become part of my daily workflow.

At the end of the day, I'm not saying AI isn't the biggest economic disruption we've ever seen. I lose sleep turning over likely scenarios myself. But I am saying we're adaptable and resilient. We will find new markets, new ways of working, and new ways to spend our time and resources.

To make those transitions happen, we need buy-in—from our peers, our colleagues, and our families. We need to show the potential and frame the opportunity. Those who resist innovation simply because they deem it a threat are doing so at their own peril on the seas of career change.

Optimism is our biggest tool in this technology shift. But blind excitement isn't enough. There's a right way and a wrong way to drive adoption.

Your organization is sabotaging tech adoption before day one

Just as a garden needs proper soil preparation before planting, technology adoption requires groundwork. Organizations consistently undermine this preparation phase in painfully predictable ways.

If you don't clearly articulate why a decision has been made to adopt a technology, your team isn't set up for success. This often comes down to who explains the decision—ideally, it should be the person who proposed it, as they have authentic enthusiasm.

An easy low-hanging fruit is to explain the scope of the adoption. Is this technology expected to be used daily? Is it already purchased and ready to deploy? Will training be required? These might seem like no-brainers, but they often get lost in the excitement and pace of a fast-working environment.

Most people want to feel involved in the decision-making process. This isn't always possible, but as leaders, we can still lead with empathy, remembering that new ways of working bring anxiety. Simply holding a town hall as a one-way monologue to announce a decision is a recipe for resistance. I've seen this happen before. Your team wants some level of agency in shaping the integration of tools, especially in creative, collaborative organizations.

Another crucial mistake is fragmenting adoption across the team. When some people adopt new tools and others are left out, it breeds resentment—even when there's a perfectly logical reason for the phased approach. Make it clear when you're running a pilot program that will expand later. Get aligned with your leadership team about what success looks like and ensure everyone knows what wins are emerging.

Do not hoard access with one or two individuals. When some team members become more efficient with the new technology, make sure everyone understands that these improvements will soon benefit the entire organization.

Effective organizations create a culture of change far before they ever implement new technology. People need to expect that adopting new tools is part of the job. And don't forget to show and tell—make time to demonstrate why these tools are making an impact.

If you don't properly prepare this cultural soil, you'll have to reengage all over again to secure buy-in every time a new opportunity presents itself. What a waste of time.

Hype-driven pitches will bite you in the ass

Let me be clear about what I mean by "hype": It's when we pitch technology based on its novelty, coolness factor, or industry buzz rather than its practical application to specific problems. Hype-led pitches sound like: "Everyone's talking about blockchain!" or "We need to be in the metaverse!" instead of "This tool will reduce our reporting time by 40%."

Selling on hype can really bite us in the ass when talking about new tech. We've seen it before with cryptocurrency, NFTs, Web3, the dot-com boom—there's always some new thing that's all hype. I've done this myself, getting excited about something without communicating the actual benefit.

Blockchain technologies and IoT followed similar patterns. Companies invested heavily because they feared missing the boat, not because they had identified specific problems these technologies would solve. The result? Half-implemented systems, frustrated teams, and wasted resources—classic signs of stalled adoption.

That said, top-performing teams need people who are motivated, curious, and excited to share their findings. Something might be a fad, but we don't want to knock down people's efforts to unearth new things. We just need to ask certain questions to cut through the noise.

If you're planting the seeds of a new tool, think about the root problem you're trying to solve. There may be several reasons, but try to solve one big problem first. Position that problem in front of your team and demonstrate how this tech will solve it.

For example, take AI note-taking software, where a bot sits in virtual calls to record and summarize meetings with action items. The root problem is simple: nobody has to take meeting minutes anymore. That's a job in itself. Often the lead of the call is responsible, which overburdens them with cognitive load and administrative tasks.

The solution is low-friction and saves time. You barely need to flag the rest of the benefits because the root benefit presents value in itself. We would be crazy not to adopt it.

It's easy to get caught up in the AI hype because much of it is intangible and buzzy—"the latest innovation to spur revenue and reduce workload." But we can't identify solutions before we identify problems.

When we get excited about using AI, we need to ask ourselves: why are we excited? Is it because everyone else is talking about it? Or because we've identified a genuine problem and found a solution?

The process should be: identify problems in your pipeline, then ask what tools can resolve those practical issues. Start with the problem, find the tool that offers a solution, then pilot research to see if it actually fixes the issue. This careful selection of the right seeds for your specific garden ensures better growth later.

Breaking down the walls between resistance and embrace

Once you've planted the seeds of new technology, the nurturing phase begins—and this is where many adoption efforts falter. Like plants that need consistent care, new technologies require attention and support to thrive.

I've experienced this resistance-to-embrace journey myself. I was an elitist about my artwork for years. I always wanted to draw with charcoal on paper, using brushes and paints. Early in my journey, I tried a Bamboo tablet and hated it. It was cumbersome, with a slippery digital pencil that lacked the tactile feeling and precision of traditional tools.

This informed my bias against digital art for years. I turned my nose up at it because my ego told me that "real artists" use traditional tools.

Years later, I picked up an Apple Pencil and started using Procreate. I was blown away by how natural and accurate it felt. Now I'm an extreme advocate for digital art because it can be based on the same foundations as traditional art while opening so many doors for creative expression.

The problem was that I let my bias from an early negative experience build a wall against innovation. Our teams can fall into the same trap—one bad experience informs their bias, and they put up walls in the face of change.

As leaders, it's our job to see past that wall and look to the future. How can this new technology solve our problems tomorrow? How do we start taking baby steps to get there? We need to nurture the growth with patience and care, understanding that adaptation takes time.

It was only through curiosity and small steps that I gained the buy-in to see the ease and beauty of the digital medium. Frankly, I carry shame when I think about my resistance to digital art but it helps me understand where people are coming from when they resist new technologies.

When digital art first emerged, many in the art community rejected it as a whole. "Computer-generated art could never replace the beauty and humanity of traditional forms," they said. "It's a parlor trick that will make artists lazy."

But look at the medium now—opportunities flourished, use cases multiplied. The same will happen with AI-generated art, despite today's controversy. Innovation and opportunity exist behind that wall of bias, just as we're seeing with all forms of AI technology.

Sandbox environments make or break your adoption strategy

Just as gardens need weeding to remove what doesn't belong, technology adoption processes need regular pruning and management. Sometimes it's important to pilot new systems within a small scope of work to identify drawbacks and potential. This might be a real client project or just an internal initiative—the point is to reduce risk in a safe environment.

Honestly, this is less about reducing risk and more about the buy-in aspect. You want your team to feel comfortable. There's a time and place to "break stuff and resolve it later," but I've found that starting small and scaling up once you've identified potential creates less chaos for a functional team.

One counterintuitive approach: avoid adopting multiple tools at once. There's an important rule in technology design that you shouldn't add too many steps for the end user or create friction. The more tools you adopt simultaneously, the more friction you create.

Keep it simple. Adopt one tool at a time and refine your process to as few steps as possible. Regularly weed out unnecessary complications and streamline the process.

Most tech adoption fears mask a deeper human truth

Every garden faces challenges from the surrounding environment. In technology adoption, that environment is often filled with fear and resistance.

You'll always encounter resistance with technology adoption. Let's not beat around the bush—in the current environment, people are afraid of losing their jobs to AI. They throw up all sorts of barriers: security concerns, ethical concerns, environmental concerns.

Yes, those concerns are valid, but we need to be real. What people truly fear is becoming redundant. That's a legitimate concern.

As leaders, we need to acknowledge this feeling and communicate candidly. If you're an empathetic leader, your purpose isn't to reduce headcount but to support your team—to eliminate redundant tasks, enable more fulfilling work, and grow the organization. Along the way, we'll find ways to increase revenue and profit.

Will it impact headcount? We don't know, because we don't know if revenue will increase or decrease. There are too many variables to predict, and I don't think anyone has the answer today.

Is any of this comforting? I don't know, but talking about it helps.

Effective organizations are values-based. When implementing change, always refer back to organizational values and how the change facilitates the goals you're trying to achieve together. Those who are aligned with the organization's goals and embrace change will be fine.

In 15 years of working in agencies, there's always been some paradigm shift that changed how we work. I remember when responsive design emerged—suddenly it seemed like we were doing triple the work for the same cost. But over time, the tools got more sophisticated, the designs more innovative, our users better served, and our clients happier. Revenue went up.

The initial resistance always transforms, but you'll only recognize that in hindsight.

We've survived every tech revolution and will survive this one

The comforting part is that this cycle is nothing new. It's like the changing seasons in a garden—predictable and necessary for growth. The automobile replaced the carriage. Television replaced radio. Computers replaced the printing press. The internet killed the video star.

The machine revolution replaced farmers, and nobody could fathom a post-farm world. The information age replaced machine operators as the main economic driver. Now we're experiencing another technological revolution, and our world will be different again.

Luckily, we're a generation raised to deal with change and adapt. They should call millennials the "chameleon generation," because that's what we truly are.

Small changes harvest bigger results than grand initiatives

The final phase of any garden is the harvest—reaping the benefits of your careful preparation, planting, and nurturing. In technology adoption, this means seeing the measurable results of your efforts.

Create a series of micro-changes at the start. Don't splash cold water on your team—lay out an adoption plan with a test pilot period, facilitate feedback, and then outline additional steps for full adoption once you've listened to your team.

Let your team trial the tools themselves for specific tasks. Ask for preliminary thoughts and hold them accountable to just trying. Even if something breaks, we must be fearless in this competitive world.

To measure success, I like surveys—they're boring but easily measurable. Google Forms takes five minutes to set up. Ask questions like: What is your understanding of this technology? Rank its usefulness. How skilled are you with it? How much time will it save?

Send the survey at the beginning and end of the pilot period. Sentiment might initially drop, but usually you see a return to positivity by the end of these cycles. Also set tangible goals: hours saved, dollars saved, project costs reduced by 10%. Make your objectives clear as day.

Watch for the warning signs of stalled adoption: high initial interest with rapid drop-off in usage, low team engagement rates after the first month, a bloated technical stack with overlapping tools, increased internal frustration, and even attrition. When you see these indicators, it's time to reassess your approach and refocus on practical benefits rather than excitement.

How do you know when you've achieved genuine adoption rather than superficial compliance? It's simple: genuine adoption happens when your team starts sharing the technology with peers unprompted. When engagement rates remain high after the initial rollout. When you start hearing unsolicited feedback about how to expand the tool's use. These are the signs that the technology has moved beyond mandated usage to becoming a valued part of your team's toolkit.

Without buy-in nothing else matters

In summary, buy-in is everything. Get in front of it with your team. Create a culture of change at your organization. Talk openly about the realities of these AI technologies—it's immensely better to express feelings than to bottle them up.

We're facing the biggest economic shift from technology in our lifetimes. We'll see some crazy stuff, let's be real. But we can make this shift easier for our teams by expressing empathy and showing the benefits to secure their buy-in.

And then, as with any garden, the cycle begins anew. New technologies will emerge, requiring fresh preparation, planting, nurturing, and harvesting. This isn't just about surviving disruption—it's about thriving through each season of change.

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If you found this valuable, I'd love for you to support my little newsletter. Frontier Notes explores the intersection of technology, leadership, and the future of work without the usual hype or doom-spiraling. Just practical insights from someone in the trenches.

Thanks for spending part of your Saturday with me and my thoughts.

P.S. If today’s insight shaved even five minutes off your mental load, please do me one quick favour—forward this note to a teammate who’s staring down the same AI storm. Every share gets us closer to 1,500 sharp-minded subscribers.

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If you find yourself nodding along, hit subscribe now—this is just the beginning of conversations we need to have about a future none of us are prepared for.

Everyone's talking about AI agents, but barely anyone knows what they are.

At least that's what the Wall Street Journal pointed out this morning in their article about the buzzword "agentic" taking over Silicon Valley. And they're right—most businesses lack a common understanding of what these agents actually are or what they'll do to our workplaces.

Look, I've been tracking this for about 10 months now, and at first, the timeline for this technology seemed like pure science fiction. But as we push into mid-2025, the pace is absolutely relentless. I still lose sleep over it.

What are AI agents, really?

Let's cut through the noise. According to the WSJ, AI agents are "systems that can take some action on behalf of humans"—like buying groceries or making restaurant reservations. But that simple definition misses the bigger picture.

Here's the thing: AI agents go beyond LLM systems like ChatGPT because they take unprompted action. They reason and act on your behalf without you having to hand-hold them through each step. They're not just glorified chatbots (which have been around forever). If you have to prompt the AI to do a specific action, it's not an agent—it's just another LLM.

Simply put, AI agents will evolve into digital colleagues. This is a paradigm shift in how we operate in the workforce.

Why the timeline should keep you up at night

I'd encourage anyone reading this to check out Leopold Aschenbrenner’s paper on situational awareness for AGI. He worked on the super-alignment team at OpenAI that managed the risks of superintelligent AI, and according to his paper, we are on course for AGI (artificial general intelligence) by 2027.

It's currently 2025. So two years from now, these AI systems will basically be able to automate all cognitive jobs. Think about that—all jobs that can be done remotely.

Right now, we're using glorified spell-checks and chatbot assistants. The more cutting-edge among us might be playing with image models like Midjourney or ChatGPT's new image generator (which is blowing up this week with all those Studio Ghibli style recreations).

It's easy to get drunk on these tools. If you're a creative person, it feels like magic. But these tools are primitive compared to what's coming next.

Where we're going with AI agents is drop-in remote workers that break from the bounds of their compartmentalized sandboxes and take control of your device. That, to me, is what defines an agent. You can onboard an agent like a human hire.

The agent will message you on Slack. It will take on projects. It will write emails, execute digital task work, and fulfill creative strategies on your behalf. Right now, you are the prompter. Soon, you'll direct the prompter.

This isn't future tech—it's happening now

The adoption is happening faster than you think. Deloitte predicts that 25% of companies using generative AI will launch agentic AI pilots this year, growing to 50% by 2027. We'll see some companies adopt these tools in the back half of 2025.

OpenAI has already released a type of agent called "Operator" that acts as a personal web assistant—sifting through content, ordering groceries, creating memes, booking appointments. This exists today.

If you code, you've probably been exposed to Claude Code. It's the first agentic AI that caught me completely off guard with its capability. Once I tried it, I burned through hundreds of dollars in API credits building powerful MVPs just to test its limits.

You give it access to your entire codebase and prompt it with things like "refactor this entire codebase into TypeScript" or "implement my static code so it works with Contentful's API." You can even run git commands in human language: "commit and deploy this code." It requires minimal prompting once you've told it what you want.

I understand the technicalities of what I'm trying to do, and I lose sleep over its capabilities and what it means for the future. Imagine how powerful that will be when these agent workers break out of their self-contained boxes. Agents will have situational awareness and understanding of our needs in ways we can't even fathom yet.

My instinct is that true computer agent capability will align to Leopold Aschenbrenner’s timeline—within the next two years—but we won't adapt quickly enough because we're distrustful and apprehensive about new technology.

Money talks, though, and nothing will hold back these floodgates. Once enough businesses start implementing agents, it will be impossible not to compete.

What gets upended first (spoiler: it's management)

So far, we've seen these tools implemented for creative use (Midjourney, ChatGPT, Sora) and now disrupting development environments. These were things we couldn't fathom being automated a few years ago.

For its next trick—and this will blow minds—it will come for management tasks. Agent workers will take over low-level HR decisions, scheduling, planning, coordinating meetings, booking events, reviewing budgets, running recurring task work, and preparing reports. I have no doubt this type of work will be driven by agents without prompting.

Remote-First vs. Office-First: Clear winners emerging

Remote-first companies will adapt quicker due to the nature of their digital work environment. Teams who are remote are already primed to work alongside agent tools.

Take note-taking bots that sit alongside video calls and meetings—standard for remote workers but less common for onsite teams. Remote companies will be the first to adopt these agents. Onsite companies should be paying attention.

Real-world impacts you can't ignore.

I see a real benefit in employing AI agents to clean up the technical debt that teams have accrued—tasks we know need to be done but don't have the time or willingness to prioritize.

This includes documenting code bases, archiving projects, organizing Google Drive file systems, organizing proposals and contracts, developing templates, documenting standard operating procedures, closing unused chat channels, and clearing out accounts for pay-per-seat software you might be overpaying for.

These tasks could take humans days to complete, but remote agents could run them automatically as part of a daily routine. If an agent can take initiative and clear out all those straggling bugs in your codebase that you've been parking for a year, that's going to reduce significant cognitive load and anxiety for your team.

The irony is that agents might also make great partners in identifying the most high-value tasks that humans can take on. If it has any self-awareness at all, demonstrating where a team thrives and developing routines that improve the business would be immensely valuable.

I'm optimistic. The opportunity isn't to replace jobs but to reduce the busy task work nobody wants to do. Yes, big companies will cut their workforce, but they will always argue that there was already fat to trim before these tools arrived.

The trade-off is that teams need to be more strategic. People who lack agency, creativity, and curiosity will have trouble adapting to the rapidly changing workforce. If you're a task worker, you need to be aware that these tools are coming whether you like it or not.

How agents will transform team dynamics

I think AI agents will be great for team alignment. They'll excel at identifying where a company isn't aligned on tasks versus the greater goals and values of the organization. They'll be good at scouring data, notifying of shortfalls on targets, and emphasizing corrective action.

To some degree, they'll replace hard conversations that humans don't like to have. That's a boundary we need to be careful about—it will be irresistible for managers to let agents do the dirty work, but we need to maintain our humanity, empathy, and candid nature. That's our advantage as humans.

The other drawback is accountability. When something inevitably goes wrong, who's responsible for a failure in the codebase, a misaligned client communication, or a major mistake in the numbers if the AI agent did all the work? Better checks and balances will need to be implemented, and human ownership will be an expectation in the future workplace.

This isn't science fiction.

These are preliminary thoughts, but none of this is fantasy. This is real. Within the next two years, you'll see much of this come to fruition in your workplace. It's better to understand it now than get caught off guard.

At the end of the day, the battlefield has changed. Your competitors are showing up with laser tanks while you're still perfecting your sword technique. The question isn't whether you'll adopt these tools—it's how quickly you'll adapt when everyone else already has.

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If you found this valuable, I'd love for you to support my little newsletter. Frontier Notes explores the intersection of technology, leadership, and the future of work without the usual hype or doom-spiraling. Just practical insights from someone in the trenches.

P.S. If today’s insight shaved even five minutes off your mental load, please do me one quick favour—forward this note to a teammate who’s staring down the same AI storm. Every share gets us closer to 1,500 sharp-minded subscribers.

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