There is a specific kind of frustration that millions of people are experiencing right now with AI, and almost none of them can name what is actually causing it.

They open ChatGPT. They ask it to do something reasonable. Schedule a meeting. Pull live data from a spreadsheet. Monitor a competitor’s pricing page. Run a workflow while they sleep. The tool does not do it. They close the tab and walk away thinking either the technology is overhyped or they are not smart enough to use it properly.

Neither is true.

The tool is not broken. They are using the wrong category of AI for the job. They do not realize there are categories at all.

This is the problem nobody addresses in the breathless coverage of artificial intelligence. The entire conversation is framed as though AI is one thing. You are either “using AI” or you are not. You are either ahead or behind. That binary creates anxiety without producing understanding. It makes people feel they need to “figure out AI” as though there were a single thing to figure out.

There is not. There are four fundamentally different tools, each designed for a fundamentally different class of problem. Most people have never been told this. So they pick whichever AI product is most popular, apply it to every situation, and conclude the technology is inconsistent when the results are uneven.

The technology is not inconsistent. The application is. Once you see the distinction, a tremendous amount of confusion falls away.

I have been in technology for over 20 years. I currently lead multiple programs developing AI models at Google. I spend my days inside the systems that most people interact with only through a chat window. What I see from that vantage point is that the gap between the people getting real value from AI and the people spinning their wheels is almost never a gap in technical skill. It is a gap in classification.

The people getting results know which tool they need before they open it. The people struggling are asking one tool to do the job of another. That is the entire problem, and it has a straightforward solution.

The simplest way I have found to explain the four categories is through a kitchen.

Imagine a restaurant. There is a recipe book on the counter, an ordering kiosk at the front, a line cook at the stove, and a head chef running the entire service. Each one plays a distinct role. None of them can do the other’s job. The moment you confuse their roles, the kitchen falls apart.

Generative AI is the recipe book. You open it, you ask a question, it gives you an answer. You provide a prompt, it creates something new: a draft, an image, a piece of code. It is powerful in its domain. But it only works when you open it and ask. It never acts on its own. It never reaches out to grab an ingredient. It waits on the counter until you pick it up.

A chatbot is the ordering kiosk. It follows a script. You press a button, it routes you to a predefined response. It does not think. It does not create. It handles a narrow, repeatable interaction with efficiency, and it breaks the moment someone asks it something outside the menu.

An AI agent is the line cook. The line cook can read the recipe book, yes. But the line cook also opens the refrigerator, grabs ingredients, adjusts seasoning based on what is available, and plates the dish. The line cook connects to the actual tools in the kitchen and takes real action. This is the category most people have not fully absorbed yet, and it is where the landscape shifts in a way that changes what is possible for a single person or a small team.

Agentic AI is the head chef. The head chef does not work a single station. The head chef runs the entire service. Delegating tasks to the line cooks, adjusting the menu mid-shift, rerouting orders when an ingredient runs out, learning from last week’s performance to improve tonight’s. The head chef plans, coordinates, and adapts in real time across the whole operation.

Recipe book. Kiosk. Line cook. Head chef.

That is the spectrum. Each role was designed for a different class of problem. The frustration most people feel with AI comes from a single, fixable error: treating the recipe book as though it were the head chef, then concluding the kitchen does not work.

What makes this distinction more than academic is where the boundaries actually sit.

Most people are already familiar with generative AI, even if they do not use that term. ChatGPT, Claude, Gemini, Midjourney. You give it a prompt. It generates something that did not exist before. This is the category that dominates public conversation about AI, and for good reason. It is genuinely transformative for any task that begins with a blank page.

But generative AI is reactive by design. It waits for you. It does not connect to your calendar, your CRM, your email, your database. It cannot monitor anything while you sleep. It cannot update a spreadsheet based on data it gathered from three different sources. It creates. That is the full scope of what it does. Understanding this single boundary prevents the most common source of AI frustration people experience today.

Chatbots occupy familiar territory. The chat window on a customer support page. The phone tree. The FAQ assistant. They have been around for years, long before the current wave. What complicates the picture is that modern chatbots increasingly use generative AI under the hood, which makes them feel smarter. They handle more natural conversation. But they remain structurally limited to responding within defined boundaries. They inform. They do not execute. The moment you need the system to actually do something rather than describe something, you have exceeded what a chatbot was built for.

The real shift happens with AI agents. An agent does not just generate text or follow a script. It connects to your tools and takes action on your behalf. The architecture is different at a foundational level. An AI agent understands your goal, plans the steps to achieve it, executes those steps across your existing software, and verifies whether the result matches what you asked for. That verification loop is what separates an agent from a simple automation. The system checks its own work.

The practical difference is not subtle. You tell an AI agent: research ten potential leads in a specific industry, review their LinkedIn profiles, draft personalized outreach emails, and add them to my CRM. Generative AI would write you a single outreach email if you prompted it well. The agent does the research, writes the emails, and places the contacts in your system. Generative AI talks about doing the work. An agent does the work. The line between these two categories is precise: the moment your task requires connecting to external software and taking real action, you have crossed out of generative AI territory.

Agentic AI extends this further, and it is the newest and most misunderstood category. It is not a single agent performing a single workflow. It is a system of agents that can reason, plan, delegate, and coordinate across multiple tasks with minimal human direction. One agent reads an incoming support email and classifies the issue. Another pulls up the customer’s order history. A third drafts a response. A fourth determines whether a refund is warranted and processes it. An orchestrating agent manages the handoffs between all of them. No human touched any of it.

The key distinctions from a standard agent: agentic AI sets its own sub-goals, breaks complex problems into smaller tasks, delegates those tasks to specialized agents, monitors progress, and learns from outcomes to adjust its approach next time. This is the head chef, not working a station but running the entire operation.

The reason this matters beyond technical curiosity is that the trajectory of this market is not speculative. It is measurable.

The AI agent market reached roughly $7.6 billion in 2025. By 2030, projections place it above $50 billion, growing at approximately 45 percent per year. Gartner projects that by the end of this year, 40 percent of enterprise applications will include task-specific AI agents, up from less than 5 percent just twelve months ago. That pace of integration has no recent precedent in enterprise technology.

The statistic I find most revealing, though, is not a market size number. It is this: roughly 79 percent of enterprises have adopted AI agents in some form. Only 11 percent are running them in production. That 68-point gap between adoption and actual deployment tells you everything about where the landscape really stands. Almost everyone is experimenting. Almost no one has figured out how to execute. The organizations that close that gap first will capture an outsized share of the advantage.

For you, whether you are building a business, refining your workflow, or simply trying to understand where to place your attention, the implication is clear. The tools exist. The confusion about which tool does what is the bottleneck. Classification is the skill that unlocks everything else.

There is a framework I use to decide which category fits any given task. It comes down to two questions.

Does this task require creating something, or does it require taking action?

Is the workflow simple, or is it complex and adaptive?

Creating something with a simple workflow: that is generative AI. Write an email, summarize a document, draft a blog post.

Responding to people with a simple, repeatable workflow: that is a chatbot. Handle the same ten customer questions, route inquiries to the right team, collect information from a form.

Taking action across multiple tools with a defined workflow: that is an AI agent. Research leads and enter them into your CRM, monitor an inbox and draft replies, compile data from several sources into a report.

Coordinating complex, adaptive work across multiple tasks simultaneously: that is agentic AI. Run an entire support pipeline, manage a multi-step content production system, orchestrate employee onboarding across departments.

Two questions. Four answers. That filter will prevent you from spending months building with a tool that was never designed for the job you are asking it to do.

Here is the point I want to leave you with, because it reaches past the tactical.

The anxiety most people feel about AI is not really about AI. It is about disorientation. The landscape moves fast, the terminology shifts constantly, and the public conversation treats it all as one enormous, undifferentiated wave. That framing leaves people feeling they need to either master everything or risk being left behind. Neither is true.

You do not need to build these systems from scratch. You do not need an engineering background. You need to understand the landscape well enough to make good decisions about where your time and energy go. The four categories, the two-question filter, and the kitchen analogy are enough to give you that orientation.

I build AI systems at Google during the day. On nights and weekends, I use these same categories to decide what to build in my own business. The difference between the people I watch struggle with AI and the people I watch get real, compounding results from it is almost never technical ability. It is knowing which tool they need before they open it.

That knowing is what separates someone who is “using AI” from someone who is using AI well. It is the difference between opening a recipe book and running a kitchen.

Now you know which role each one plays.

There is a question that has been quietly reshaping the education economy for the last several years, and most people who monetize their expertise have not stopped long enough to actually sit with it.

If anyone can learn almost anything for free, what are people actually paying for?

This is not a philosophical exercise. It is the operating reality of 2026. Large language models like ChatGPT, Claude, and Gemini can teach you more than most online courses. They can give you customized advice for your specific situation. They can answer your follow-up questions instantly. YouTube has become the largest free university in human history, with world-class tutorials on virtually every subject you can name.

The cost of accessing information has collapsed to near zero. And yet people are still spending money on education. In many cases, more money than before.

The thing they are paying for is not what most people think it is. Understanding this distinction is what separates the experts who build real income from the ones who keep launching products no one finishes.

Here is the uncomfortable truth about the online course industry.

Most people never finish the courses they buy. The data on this is not ambiguous. Self-paced online courses have completion rates that hover between 3 and 15 percent across virtually every major study conducted in the last decade. MIT published research showing that their MOOCs had a completion rate of just over 3 percent between 2017 and 2018. Udemy, one of the largest course platforms in the world, reports an 8 percent completion rate from their best-performing instructors. Harvard and MIT’s joint research found that more than half of registrants never even opened the first lecture.

These numbers are not anomalies. They are the structural reality of self-paced digital education. And they have consequences that go far beyond the buyer’s experience.

When your students do not finish your course, they do not get results. When they do not get results, they do not recommend you to anyone. When they do not recommend you, your next launch depends entirely on your ability to find new cold audiences. When you are perpetually hunting for new buyers instead of being carried by word of mouth from people who actually succeeded, your business is on a treadmill. You are running harder to stay in the same place.

The course model was built for an era when access to information was itself the value proposition. That era is over. The value proposition has migrated somewhere else entirely, and most people have not followed it.

What people actually pay for now is not knowledge. It is the environment that makes them execute the knowledge.

This is a distinction that sounds subtle but changes everything about how you design and sell an educational product. The problem your customer has in 2026 is almost never that they lack information. Their problem is that they have too much information and no structure to act on any of it. They have 47 browser tabs open. They have saved three courses they never started. They have a notes app full of ideas they have not touched in months. They know what to do. They do not do it.

The solution to that problem is not more content. It is structured accountability delivered in a live, time-bound, group environment.

That is what a cohort-based course is. And it is, I believe, the most underutilized and highest-performing format available to anyone who wants to turn expertise into income right now.

A cohort is a live, time-bound group program where a set of students go through material together on a fixed schedule. As opposed to a self-paced course where people log in whenever and work alone, a cohort creates structural forces that drive completion without relying on the buyer’s willpower.

When students are on a shared timeline, they cannot fall behind silently. When there is a live session on Tuesday, they show up on Tuesday or they watch the recording knowing everyone else has already moved ahead. When there is a community channel, they see peers posting progress, which creates social pressure to keep pace. When homework has a deadline, it gets done.

None of those forces exist in a self-paced course. In a self-paced course, the only accountability mechanism is the buyer’s discipline. And discipline, left to its own devices, is not a system. It is a wish.

The data reflects this. Programs with cohort structures, live support, and community see completion rates between 70 and 96 percent. Harvard’s altMBA, a four-week cohort teaching soft skills traditionally covered in a two-year MBA program, reports a 96 percent completion rate. Esme Learning’s cohort programs report 98 to 100 percent. When Harvard transitioned its case-method courses to incorporate peer collaboration, completion climbed to 85 percent while most of their MOOCs remained in the single digits.

The difference between 5 percent completion and 90 percent completion is not content quality. It is the format itself. The structure is doing the work.

This model is not new. That is one of the most important things to understand about it.

Every university in the world runs on cohorts. Every military training program. Every professional certification worth its weight. Every bootcamp that actually produces people who can do the job on day one. Harvard Executive Education charges $15,000 for a six-week cohort. The format has been the backbone of structured education for over a century because it works at a level that self-paced learning simply does not reach.

What changed is not the model. What changed is who can run one.

Until recently, you needed institutional backing, physical infrastructure, and administrative support staff to operate a cohort. Now you need a laptop, a Zoom account, and an audience of any size. The barrier to entry collapsed while the format’s effectiveness remained exactly where it has always been. That gap between low barrier and high performance is where the opportunity sits right now, and almost no one in the creator and expert economy is taking it seriously.

On Maven alone, the largest dedicated platform for cohort-based courses, over 12 instructors have crossed a million dollars in revenue and over 100 have crossed $100,000. Ship 30 for 30, a writing cohort that helps people publish 30 pieces in 30 days, has done over $10 million in lifetime revenue on a single, focused outcome. Building a Second Brain by Tiago Forte, a cohort that teaches people to organize their digital notes and research, has generated between five and ten million dollars. Ali Abdaal’s Part-Time YouTuber Academy has cleared $5 million helping people launch a YouTube channel and publish their first video.

Each of these programs delivers one specific outcome over a short, defined timeline. That narrowness is not a limitation. It is the design principle that makes them work.

If you are sitting on expertise worth monetizing, whether from a career, a craft, a transformation you have been through, or a skill people regularly ask you about, the cohort format gives you a structural advantage that no other model offers right now. Here are four principles that separate a well-built cohort from a live course that just happens to have a start date.

The first principle is transformation over education.

A cohort is not a course with a schedule attached to it. A cohort exists to help a specific group of people achieve one tangible result in a short period of time. The emphasis is on the word “achieve.” You are not selling information transfer. You are selling outcome delivery.

That outcome should be simple enough to state in one sentence and achievable within four to six weeks. “Publish 30 pieces of online writing in 30 days.” “Launch your YouTube channel and publish your first video.” “Build your first income system and make your first sale in 30 days.” Each of those is a clear, verifiable transformation. If your outcome description requires a paragraph of explanation, it is too complex. Narrow it down until a stranger can understand what they will have accomplished by the end.

The second principle is what I call Short, Narrow, Small.

Short means four to ten hours of live content delivered across two to four weeks. Narrow means you solve one problem and deliver one outcome. Small means you include only two to four core features: live sessions, a handful of resources, homework assignments, and a community space. That is it.

The instinct most people have is to add more. More modules, more bonuses, more content, more weeks. That instinct will destroy your cohort. The single greatest reason people abandon educational products is information overload. People feel good when they complete something. Your cohort’s entire job is to be completable. If you feel the need for more sessions or a longer timeline, you are teaching too much. Make it smaller.

The third principle is systems over curriculum.

Here is the truth that most educators resist: people do not want to learn. They do not care about your cohort, your framework, or your teaching style. They care about the result. Your job is to make the path to that result feel as close to done-for-you as possible, even though it is not a done-for-you service.

The way you do this is by building shortcuts into every step. Templates that are 80 percent complete so students only fill in the remaining 20 percent. AI prompts that generate first drafts they can edit rather than stare at blank pages. Checklists that eliminate decision fatigue. Examples pulled from real results so students can model their work on something proven rather than creating from scratch.

The best cohorts do not just teach. They hand people a machine that does most of the heavy lifting for them. That is what makes the difference between a student who understands the material and a student who actually executes it.

The fourth principle is sell before you build.

You do not need a completed curriculum to launch a cohort. You need an outline, an orientation video, and a clear description of the outcome. Sell the first cohort on that basis. Build the detailed slides and session content after you have confirmed there is demand.

This is one of the most powerful features of the cohort model: it has a built-in pre-sale mechanism. Because sessions are live and scheduled in the future, people must buy before the program begins. You validate demand before you invest the time to build. If nobody joins the waitlist, the concept or value proposition needs work. If people sign up, you build with confidence knowing real humans are waiting for what you are creating.

I spent four months designing my program before I launched the first cohort. I pressure-tested every component against twenty years of experience building and running programs at Google, Deloitte, and across government, healthcare, and enterprise technology. You do not need four months. But you do need to understand that a cohort is not something you throw together over a weekend. It is a structured system designed to produce a specific outcome. Treat it that way and the results will follow.

There is a flywheel embedded in this model that most people do not see until they experience it.

When your students complete the program, they get results. When they get results, they tell other people. When other people hear about it from someone they trust, your next cohort sells faster with less effort. When each cohort sells faster, you can raise the price because the product has a track record. When the price rises alongside demand, you have a real business, not a launch treadmill.

Compare that to the traditional course model. You spend months creating content. You launch it. Five percent of buyers finish it. Almost none of them get the result. No one recommends it. Your next launch requires the same cold outreach, the same ad spend, the same exhausting promotion cycle. The format itself is working against you.

A cohort reverses that dynamic entirely. Better outcomes lead to organic demand. Organic demand leads to sustainable pricing. Sustainable pricing leads to a business you can actually run without burning yourself out every quarter.

The market conditions supporting this shift are not going to reverse.

AI has made information functionally free. YouTube has made instruction functionally free. What remains scarce is structure, accountability, human interaction, and the kind of guided environment that makes people actually do the work they have been putting off. That scarcity is real, it is growing, and it is exactly what a cohort provides.

The people who recognize this early are building the next generation of education businesses while everyone else is still trying to sell pre-recorded content into a market that has moved on.

I have run two cohorts of my own program so far, and our successful candidates make their first sale within 30 days. I am not sharing theory. I am describing what I do on nights and weekends while still working full-time leading AI programs at Google. The model works because the format itself drives the outcomes. Not motivation, not content quality alone, but the structural mechanics of shared timelines, live interaction, and peer accountability doing what willpower never could.

If you have been sitting on expertise you know is valuable, if you have considered building a course or an info product but something about it has never felt right, if you have watched the market shift and wondered where the real opportunity is heading next, this is the direction it is moving.

I put together a free blueprint that walks through the complete system for building a cohort-based course from scratch, including the structure, the launch timeline, the marketing sequence, and the templates I use in my own program. You can get it at guneytopcu.com/cohortblueprint.

The window on this is open right now. The format is proven, the tools are accessible, and the market is ready. The question is whether you move on it while the space is still uncrowded or whether you wait until everyone else has figured out what you are reading right now.

Until next Monday,

Guney

Not enough jobs, not enough capital, not enough opportunity, not enough room at the top. Your entire career strategy, your financial planning, your sense of what makes you valuable in a market... all of it was shaped by that assumption. Compete harder. Specialize deeper. Protect your position. Accumulate while you can.

That assumption is breaking apart. Not in some distant, theoretical future. It is breaking apart right now, in ways that most people won’t recognize until the transition is already behind them.

Here is where we’re standing in early 2026.

AI is compressing the cost of production, knowledge, and distribution across every industry it touches. The things that used to be expensive are becoming cheap. The things that used to be rare are becoming common. The constraints that shaped the careers of everyone who came before you are weakening by the quarter.

Manufacturing costs have dropped 40 to 60 percent in sectors where AI optimization is deployed. Research that required teams and months now happens in minutes. A single person with the right tools can produce what used to require a department of twenty.

These are not projections about tomorrow. They are the conditions you are operating in today.

Sam Altman, the CEO of OpenAI, framed this trajectory in terms worth sitting with. He wrote that by 2035, any individual should be able to marshal intellectual capacity equivalent to what all of humanity possessed in 2025. He described a future defined by massive prosperity, where the ability for one person to accomplish dramatically more than they could even five years ago will be, in his words, a striking change. He acknowledged that the long-term changes to our economy will be enormous, while emphasizing that agency, willfulness, and the ability to decide what to do in a constantly shifting world will hold tremendous value.

Elon Musk went further at the World Economic Forum earlier this year. He described a near future where ubiquitous AI combined with ubiquitous robotics creates an economic expansion that has no historical precedent. His framing was blunt: you cannot maintain a system where essential work falls only to some people while abundance is theoretically available to all. The two are incompatible, and one of them gives way.

The economic orthodoxy of the last three centuries insists that abundance is a dream and scarcity is the permanent condition. The people actually building the systems driving this transformation say the opposite. One of those perspectives is wrong. The question that should concern you is which one, and whether you have positioned yourself for the answer.

Here is what most people miss about this shift.

When they hear “post-scarcity,” they picture utopia. Everything is free, nobody works, the whole picture has a science-fiction glow to it. That is not what is happening, and misunderstanding this is where most people’s thinking goes off the rails.

Post-scarcity does not mean scarcity disappears. It means scarcity reorganizes. The constraints do not vanish from the system. They migrate to a different part of it.

A research paper published through SSRN late last year made this distinction sharper than anything I have encountered in mainstream commentary. The author argued that AI delivers what he termed “technological abundance,” meaning cost compression and capability expansion at a scale we have never seen. He then drew a line that most people miss entirely: technological abundance is not the same as economic abundance. The costs drop and the capabilities expand, yet new bottlenecks form at different layers. Infrastructure, institutional trust, organizational know-how, enforcement capacity. The scarcity does not die. It moves to a different floor of the building.

This is the part that most commentary on AI gets completely wrong. The mainstream frames this as a binary: either AI makes everything abundant and we all win, or AI takes all the jobs and we all lose. Neither framing captures what is actually unfolding. The real story is one of migration. Value is moving from the layers AI can handle to the layers it cannot. If you understand where that migration is headed, you can position yourself in its path. If you do not, you will spend the next decade optimizing for a game that no longer rewards the moves you were taught.

Peter Drucker saw this migration decades before AI made it visible.

In 1993, he wrote that knowledge had become the central resource of economic life. Not a resource among others, but the resource. He warned that knowledge only yields value when it is applied toward results, and that it must be improved and challenged constantly, or it vanishes.

Over thirty years later, the vanishing he warned about is unfolding in real time, though not in the way most people would expect. Knowledge is not disappearing. Its scarcity value is. When any person, or any machine, can access domain expertise instantly, knowledge on its own stops functioning as competitive advantage.

An essay published through the Peter Drucker Forum earlier this year extended this line of thinking considerably. The authors argued that we have crossed the boundary of Drucker’s original framework. The knowledge worker, the figure who defined the economic landscape of the 20th century, is no longer the leading edge. What is emerging in that role is someone the authors called “the Value Creator,” a person whose worth comes not from possessing knowledge but from transforming knowledge into new forms of value under conditions of uncertainty.

That distinction carries more weight than almost anything else in this newsletter. The economy of the last several decades rewarded knowledge possession: what you knew, what credentials you held, what expertise you could demonstrate. The economy taking shape right now rewards knowledge transformation: what you can build with what you know when the rules are ambiguous and the path forward is not obvious.

There is a case study that illustrates this migration better than any theory.

In 2016, Geoffrey Hinton, one of the founding figures of deep learning, made a public statement that received widespread coverage. He told people to stop training radiologists because AI would outperform them entirely within a matter of years. By 2017, a model called CheXNet was already beating radiologists on specific diagnostic benchmarks.

That was a decade ago.

Today, demand and wages for radiologists are at all-time highs.

The models did improve. The narrow technical predictions were, in a strict sense, accurate. Yet the humans became more valuable, not less. The reason is that real-world medical imaging involves variation, ambiguity, liability, patient context, and clinical judgment that no current system handles autonomously. AI took over the volume work, the screening, the pattern matching at scale. The radiologist became the person who handles everything requiring judgment under uncertain conditions.

The commodity layer got compressed. The premium layer expanded. This same dynamic is repeating in every industry AI touches. Stock photography collapsed under AI image generation, while editorial photographers who create emotionally resonant, conceptually distinctive work saw their rates climb. Basic legal research is being automated at speed, while lawyers who navigate ambiguous, high-stakes negotiations are commanding higher fees than ever. Content production costs are falling through the floor, while creators who have built genuine trust with their audience are charging more.

The pattern is consistent enough to treat as a principle: when AI compresses the commodity layer of any field, the premium layer, the layer built on human judgment, creativity, and trust, expands to absorb the value that was released.

So here is the question that changes your strategy for everything.

What becomes truly scarce when traditional resources are abundant?

When production is abundant, curation becomes scarce. When information is abundant, trust becomes scarce. When content is abundant, authenticity becomes scarce. When AI handles most tasks, human creativity and judgment become scarce. When access to tools is universal, the ability to know what to build with them becomes scarce.

This is the new scarcity. Not physical resources, not information, not production capacity. The new scarcity is trust, creativity, judgment, and authentic human connection.

If you orient your skills, your business, and your career around these new scarcities, you are not simply adapting to a transition. You are positioning yourself at the center of where value flows next.

Drucker wrote something in 1985 that keeps resurfacing in my thinking as I watch these shifts accelerate from the inside. He said that the greatest danger in times of turbulence is not the turbulence itself, but to act with yesterday’s logic.

Most people are still acting with yesterday’s logic. Not because they lack intelligence or awareness. Because the old logic worked so effectively, for so long, that it stopped feeling like a strategic choice and started feeling like truth. It was never truth. It was always strategy, a strategy adapted to specific conditions. Those conditions are now shifting under everyone’s feet.

Most people will hear this argument and nod along. It makes intellectual sense. Then they will go back to optimizing for the old game the next morning.

They will continue grinding to accumulate skills that AI is already absorbing faster than any human can keep pace with. They will continue chasing volume in a world that is drowning in volume. They will continue competing on speed and output against systems that will always be faster and more prolific than any person alive.

The problem is not a lack of understanding. The problem is that their entire operating system, the mental model they use to make decisions about career, time, and energy, was installed during the scarcity era. That operating system is still running in the background, still whispering the old instructions: specialize narrowly, produce more, compete harder, protect your position. The operating system itself has become the liability.

The replacement is not complicated, but it requires a different kind of honesty than most people are accustomed to.

You have to ask yourself what you are actually good at. Not what your resume says, not what your job title describes, but what you do that creates value in ways that cannot be replicated by a system running on statistical prediction and data matching.

For most people, the answer is closer than they expect. It lives in the judgment calls you make that no playbook covers. In the way you read a room, a conversation, or a situation and understand what is really happening underneath the surface. In the ability to take disconnected information from different domains and synthesize something that did not exist before.

These qualities are not soft skills being polite about their irrelevance. In a post-scarcity economy, these are the hard skills. These are the genuinely scarce resources.

I lead programs building AI models at Google. I work inside these systems every day. I see what they can do, and I see, with equal precision, what they still cannot do. The gap between AI capability and human capability is narrowing rapidly on the production side: generating content, analyzing patterns, executing defined tasks. On the judgment side, the creativity side, the empathy and relational side, that gap is not narrowing. It is widening. That widening gap is where value lives now and where it will increasingly concentrate.

Altman himself acknowledged this dimension in a conversation with Adam Grant. He observed that the mutual care between people, the degree of attention we pay to each other’s actions, and the deep human desire to interact with other humans will become more important in the age of AI, not less. He described AI as an extraordinary tool while making the point that the people directing that tool, their intentions, insights, and empathy, will only become more precious as the tool grows more capable.

There is a practical dimension to this that extends beyond philosophy and into how you allocate your time and energy starting now.

The old economic model rewarded ownership. Buy assets, stockpile, lock in your position. When things are genuinely hard to get, that instinct serves you well. In an abundance economy, ownership becomes weight. It is inflexible, it ties your resources to depreciating structures, and it limits your ability to move when conditions shift, which they will, repeatedly. What is replacing the ownership model is access: computing power on demand, design tools on demand, manufacturing capacity on demand, professional services on demand. The individuals and companies that thrive in the emerging landscape will not be the ones who own the most infrastructure. They will be the ones who maintain the most flexibility to adapt as the environment continues to change.

The same principle applies to your career architecture. Deep specialization in one narrow domain that AI might absorb entirely is a wager against the direction things are moving. Transferable judgment, precise communication, systems-level thinking, and the ability to direct AI tools as force multipliers represents a wager in its favor. The person who can think across systems, communicate with precision, solve ambiguous problems, and use AI to multiply their output is not being replaced by any of this. That person is becoming the most valuable participant in any organization, any market, any economy being reshaped by abundance.

There is one more dimension worth sitting with, and it is the one that receives the least attention despite carrying perhaps the most consequence.

When AI floods the world with content, information, and media at volumes no previous generation could have imagined, attention does not become abundant alongside everything else. It becomes the most constrained resource in the entire system.

We are approaching a world with more content than any human could consume in a thousand lifetimes. The bottleneck is no longer production. It is trust. It is genuine human connection. It is the willingness of another person to give you their time and their focus because they believe you have something real to offer, something that was not generated on a whim and published without thought.

The first iteration of the attention economy was built on volume. More eyeballs, more impressions, more output. That model is reaching its natural limit. When everyone can produce unlimited content with minimal effort, volume stops functioning as competitive advantage. What is replacing it is something fundamentally different: deep engagement over broad reach, authenticity over production polish, community and earned trust over viral mechanics.

If you are building anything right now, whether it is a career, a body of work, a business, or a reputation, this is the shift to absorb completely. The people who define the next decade will not be the ones who produce the most. They will be the ones other people actually trust. That is a fundamentally different game from the one most people are still playing, and the sooner you recognize the difference, the sooner you can stop wasting energy on moves that no longer compound.

Here is what I want to leave you with.

We are not waiting for a post-scarcity economy to arrive. We are already standing in the early innings of one. The economic assumptions that most people operate under, compete for limited resources, specialize narrowly, optimize for the system as it currently exists, are becoming liabilities faster than they are becoming visible.

The shift runs in one direction. From scarcity thinking to abundance positioning. From producing to curating. From volume to depth. From rigid infrastructure to adaptive systems. From competing on what AI does well to investing in what AI cannot touch.

You do not need to see the entire future with precision. You need to see the direction of the shift and begin moving accordingly.

The question is not whether this transition is happening. It is whether you will be positioned on the right side of it, or whether you will find yourself scrambling to adapt after the rules have already been rewritten around you.

Until next Monday,

Guney

Your direct deposit hit this morning. You checked. The number looked fine. You closed the app.

But the feeling didn’t match the number.

There’s a version of this that plays out every two weeks for millions of people. The money arrives, the math works, the bills get covered. And yet there’s this low hum underneath it all, this sense that no matter how many…

Read more

Read more

There is a question running underneath everything you build.

Most people never chose it consciously. They inherited it from whoever taught them. They absorbed it from the culture around them. They defaulted to it because no one ever suggested there was an alternative.

But this question is not just shaping what you build. It is shaping who you are becoming…

Read more

In the first, you are the author. You decide what you will become. You choose what will shape you. You look at the hours available and deliberately allocate them toward something that demands your growth. Time passes, and you are different at the end than you were at the beginning, and the difference is one you ch…

Read more

There’s a conversation happening right now that I think most people are misreading.

The argument goes something like this: “How-to content is dead. AI can generate it instantly. The barrier to sharing useful information has collapsed to zero, so nobody values it anymore.”

On the surface, it sounds right. Open any platform and you’ll drown in “7 ways to do…

Read more

There is a particular kind of Sunday evening that most ambitious people know intimately.

You spent Saturday morning with good intentions. You opened your laptop. You told yourself this would be the weekend you finally started. Maybe it was the side project you have been thinking about for months. Maybe it was the content you keep meaning to create. Maybe…

Read more

Why it matters: The flood of AI-generated content is already here. Those who remove themselves from their work become interchangeable. Those who keep the human core intact become irreplaceable. The difference is not in the tools yo…

Read more

The economics of building a business changed underneath everyone’s feet, and most people haven’t noticed yet. They’re still operating on assumptions from 2019, still believing they need a team, a budget, a runway, permission.

What used to require five people, two years of saving…

Read more

It makes you feel like you’re doing something without requiring you to do the right thing. And in the age of AI, it’s becoming the most expensive way to stay exactly where you are.

You woke up early last Saturday. Told yourself this was the weekend you’d finally make progress. By noon, you had 14 tabs open, watched two…

Read more

We are told it is loud. Dramatic. Full of breakthroughs and revelations. Late nights fueled by inspiration bolts from the sky.

But look closer at the people who actually built something lasting. What you find is not what you expect.

You find boring.

The same simple actions repeated every single day. Routines so mundane…

Read more

Read more

It was fun writing this week’s piece (wanted to be a bit dramatic and get you thinking!). Hope you enjoy it.

Read more

Subscribe now

Understanding the Fundamentals of Product Development

Traditional product development follows a rigid sequence. Teams spend months documenting requirements…

Read more

AI isn’t just democratizing entrepreneurship. It’s eliminating the barriers that kept your valuable skills locked inside your 9-5.

Not eventually. Right now.

In this article, you’ll see exactly what to do about this opportunity. Not “what it is.” Not “why it matters.” What to DO.

Because the conversation about AI has been bac…

Read more

Subscribe now

Your notes app is full of ideas you’ll “get to eventually.” Your bookmarks folder has 73 tabs of resources you swear you’ll read. Your YouTube history is a graveyard of “10 Steps to Success” videos that inspired you for exactly 48 hours be…

Read more

This isn’t a feel-good underdog stor…

Read more