10,000x Knowledge Worker: How History's Forgotten Productivity Secret Reveals AI's True Potential

There Are 5 Levels Of AI Productivity, And Most People Are Stuck In Level One, But They Have No Idea

In 2026, an individual will launch a billion-dollar company with AI, according to the CEO of Anthropic (creator of Claude).

Perfecting individual AI interactions via prompts won't be what makes this possible. Rather, it'll be about orchestrating hundreds of agents in concert.

Today's knowledge workers perfecting their ChatGPT prompts alone are like factory workers in 1913 trying to work faster at their individual stations. The real 1913 breakthrough came when Henry Ford stopped asking questions like:

"Workers spend 50% of their time walking. How can they move faster?"

And started asking questions like:

"Why are workers moving at all? What if we created a machine that brought the work to them?"

Ford didn't just solve the problem, he redefined it. In so doing, he created unprecedented productivity gains that seemed impossible under the old paradigm.

Similarly, the breakthroughs today will happen when knowledge workers stop asking:

How can I build the perfect prompt?

And start asking:

How can I build a human-AI workforce organizational chart such that AI agents can manage AI agents?

The paradigm shift from using AI as a tool to collaborating with AI as a workforce is already underway, and it's more radical than most realize.

As the video below shows, the vanguard is already experimenting with orchestrating more than a dozen Claude agents processing complex workflows for hours with minimal intervention. Their next target? 50 parallel agents.

Source: thermo

The video not only illustrates what’s possible today. It shines a light on the growing and hidden divide between knowledge workers at the AI frontier and those skeptically wading into ChatGPT a few times per week, worried about hallucinations.

Among many AI lab leaders, knowledge work is already a solved problem. No technological breakthroughs needed. It just needs a few years to be built. This clip from a senior technical lead at Anthropic, Sholto Douglas, conveys the level of conviction that many in the field now have:

Source: Unsupervised Learning Podcast

These predictions should stop every knowledge worker in their tracks:

 I think we're near guaranteed at this point to effectively have models that are capable of automating any white collar job by 2027 or 2028, and or near guaranteed [by the] end of decade.

We haven't yet hit like an intellectual ceiling on the tasks which we we're able to teach to teach the models.

I think most people in the field currently believe that pre-training plus RL [reinforcement learning] are themselves sufficient to reach AGI.

Every piece of evidence I see says that.

TIMELINE NUANCE 

Not everyone agrees on such short timelines. One of my favorite AI thinkers, Dwarkesh Patel, recently wrote a thought-provoking article challenging the consensus emerging in AI labs, and his essay has gotten interesting responses from other AI luminaries like Andrej Karpathy (formerly at OpenAI and Tesla). However, what’s important to understand is that, in the big picture, the disagreements are relatively minor. Whether AI replaces knowledge work in 2027 or 2032, historic changes are coming way faster than most people realize.

The Two Real Questions For Knowledge Workers Now Are…

Question #1. Who will discover how to manage AI the way Henry Ford discovered the assembly line?

Question #2. How can we increase knowledge work productivity en masse rather than just for a few elite performers?

I've spent the last two years obsessing over these two questions. What I've found is a predictable progression—five stages that compound on each other, taking us from today's incremental gains to tomorrow's 10,000x leverage:

• Prompt Engineering (10x leverage) — Most of us are here

• Infinite Prompting (100x total) — We're entering this now

• Model Management (1,000x total) — Running multiple thinking AIs in parallel

• Model Leadership (10,000x total) — AIs managing other AIs

• Autonomous Firms (unknown) — Companies without humans

In this article, I’ll share the math and the historical precedent that led me to this model. Then, I’ll share an overview of each stage, so you can begin applying its lessons.

But first, I need to tell you about a quote that changed how I see everything.

The 50x Productivity Miracle That Changed Everything

Reading the following quote in 2022 changed my life:

The most important, and indeed the truly unique, contribution of management in the 20th century was the fifty-fold increase in the productivity of the manual worker in manufacturing.
—Peter Drucker (one of the most respected management thinkers in history)

The significance of this quote cannot be overstated.

Drucker isn’t just talking about 50x productivity for the smartest people. He’s talking about 50x productivity on average for an entire society.

Compared to previous productivity gains, the 20th century is a historical aberration.

Source: A Simple Combinatorial Model of World Economic History

Therefore, it’s worth understanding why it specifically happened during this time period and not others. The more we know about it, the more we can build upon it today.

This productivity explosion is a big deal because productivity is essential to individual and collective prosperity.

On an individual level, we don’t appreciate how difficult life was before the boom. For example, “In 1870, farm and urban working-class family members [in the United States] bathed in a large tub in the kitchen, often the only heated room in the home, after carrying cold water in pails from the outside and warming it over the open-hearth fireplace,” according to economic historian Robert Gordon. That was just 150 years ago!

On a societal level, if productivity drops for too long, revolutions happen. If productivity goes up, the system stays because workers enjoy higher incomes and more leisure time. Drucker believes that had the productivity revolution not happened, capitalism might have fallen to Communism during the Great Depression.

Today, we’re at a similar do-or-die moment, but with different consequences. Roughly 13% of the government’s budget in 2024 was spent on paying off our debt, and this number is increasing exponentially and may soon become unsustainable. Losing hope in the government's ability to curb spending, many business leaders now believe that the only path forward is to drastically increase our collective productivity with AI.

Enamored by what really caused the 20th-century productivity revolution and wanting to understand why productivity stalled after 1970, I spent over 300 hours researching the history of the Industrial Revolution and wrote a series of articles, starting with In 1911, A Genius Revealed a Forgotten Science of How to Be 50x More Productive Without Working More Hours. The post had 17,000+ claps, which means I wasn’t the only one fascinated by the topic.

Fast forward to today.

We're standing at the beginning of a 100 times bigger and faster productivity revolution thanks to AI. And the exact lessons from the manual work productivity revolution are directly applicable to us collectively and individually…

My #1 Lesson From Spending 300 Hours Studying The 20th Century 50x Productivity Boom

Before my deep dive, I had naively assumed that the #1 cause of the Industrial Revolution was technologies like electricity (cheap, reliable, distributed) and machinery.

I was wrong.

What I now see is that technology created the possibility, but what really catalyzed that possibility into productivity were new and counterintuitive management philosophies that iteratively built on each other over decades of experimentation:

• Scientific Management

• Lean Production

• Six Sigma

• Extreme division of labor

• Standardization

• Kaizen

• Total Quality Management (TQM)

• Standard Operating Procedures (SOPs)

These philosophies did not just dictate how we used existing technology. They also led to the creation of new tools (eg, assembly lines, single-function machines, interchangeable parts).

Said differently…

The industrial revolution breakthrough wasn't just about having new technology like electricity—it was reimagining the entire concept of work itself. Factory owners who simply replaced steam engines with electric motors saw minimal gains. But those who questioned fundamental assumptions about how work should flow? They changed the world. No one exemplified this mindset shift better than Henry Ford, whose insights about worker productivity would revolutionize not just his factory but the entire industrial economy.

His lessons are particularly relevant today as we rethink knowledge work with AI from the ground up.

Case Study: How Henry Ford 4x’d The Productivity Of A Simple Job

One of Ford’s most famous and unique management innovations stemmed from analyzing how employees spent their time and realizing that half of a worker’s time was wasted simply walking between materials and tools:

The undirected worker spends more of his time walking about for materials and tools than he does in working; he gets small pay because pedestrianism is not a highly paid line.
—Henry Ford

So Ford had the work come to the workers rather than them walking to the work. This innovation was known as the assembly line.

While Ford is known for the seemingly simple idea of the assembly line, what is much less well known is that it took years of experimentation for him to create the necessary systems and insights to make it obvious.

For example, in his autobiography, he shares a step-by-step overview of how he turned a 20-minute assembly job into a 5-minute one (a 4x improvement):

Let’s break this example down.

First, taking a one-person job and splitting it into 29 separate jobs requires a sufficient scale to employ 29 separate full-time employees.

Second, it requires a sort of maniacal focus on improvement, radical openness to starting over, and active exploration what’s possible. Describing his philosophy, Ford said:

We try everything in a little way first—we will rip out anything once we discover a better way.
—Henry Ford

I refuse to recognize that there are impossibilities. I cannot discover that any one knows enough about anything on this earth definitely to say what is and what is not possible.
—Henry Ford

As it comes to knowledge work, almost no one has experimented with extreme specialization like Ford did. As a result of his experimentation on specialization, Ford saw the following benefits:

• Less training was required, allowing someone to walk off the street and be immediately productive.

• Fewer errors happened because the person could focus on one thing.

• Productivity increased because the person just had to master one task.

Next, the example above shows Ford’s attention to detail. He measured productivity to such a degree that he experimented with different assembly line heights and speeds to find the optimal approach. Even today, no knowledge work companies come close to achieving this level of precision in optimizing their work processes.

For AI to turn into productivity, we need Henry Fords of knowledge work. People who don’t just use AI, but constantly improve how they use AI. People who explore what’s possible rather than just copy best practices. And people who think from scratch.

To understand how Ford thought from first principles, consider the image below of an 1830 steam-powered cotton factory:

Interior of the Imperial Fez Factory (1880-1893)

Rather than being organized based on the order of work, the machines were organized based on their distance from the central axle, which was powered by steam.

MIT and Stanford researchers Andrew McAfee and Erik Brynjolfsson provide a great overview of how these factories worked in The Second Machine Age:

In a steam engine–driven plant, power was transmitted via a large central axle, which in turn drove a series of pulleys, gears, and smaller crankshafts. If the axle was too long the torsion involved would break it, so machines needed to be clustered near the main power source, with those requiring the most power positioned closest. Exploiting all three dimensions, industrial engineers put equipment on floors above and below the central steam engines to minimize the distances involved.

The farther away from a central axle a machine was, the more belts and gears it needed. The more belts and gears it needed, the more friction there was, which lowered the efficiency of energy use.

In this paradigm, the assembly line was not intuitive or obvious. Putting the machines in the order of work didn’t make sense. Rather, it took someone like Ford to make the connection and iterate toward it over years.

Finally, consider that part of Ford’s innovation wasn’t just how he arranged tools, it was how he compensated people.

After the release of the assembly line in 1913, profits shot up to $27 million (about $588 million in today’s dollars) from $13 million the year before.

A year later, Ford famously doubled the wages of all factory employees. The historic change led to a surge in people wanting to work at the factory and the creation of a middle-class factory workforce.

For context, below is a photo of a crowd of job applicants during the same month that the wage increase happened:

Source: The Henry Ford Museum

What’s less well known is that Ford didn’t have a choice but to increase wages.

Employees hated doing repetitive tasks on an assembly line, and the turnover rate was 370%. This meant that the average employee only lasted a few months. Not only that, in the spring of 1913, the union successfully shut down one of the factories for a short time. With this level of turnover and turmoil, the assembly line business model didn’t work, and it risked work stoppages.

With increased wages, the assembly line transformed from a source of alienation into a pathway to prosperity. Workers now saw that machines weren't replacing their value—they were multiplying it.

This story from 1913-1914 might seem like ancient history, but it's actually tomorrow's headline. Replace "assembly line" with "AI agents," "manual workers" with "knowledge workers," and "370% turnover" with "massive unemployment," and you're looking at our immediate future.

What The 50x Productivity Revolution Teaches Us

Taking a step back, the assembly line case study, specifically, and the 50x productivity revolution more broadly, provide us with important insights into how productivity might be increased with AI. More specifically:

• Management Innovation, Not Just Technology: The biggest gains won't come from better AI models alone, but from revolutionary management frameworks we haven't invented yet. Today's prompt engineering is like the steam-powered factory—functional but primitive compared to what's coming. The winners will be those who discover the AI equivalent of the assembly line.
  

• Questioning Core Assumptions: We'll need to look beyond the assumptions we have about how work gets done. Just as Ford questioned why workers should walk to materials, we need to question why humans should manage every AI interaction, why we work on one task at a time, or why we limit ourselves to human-speed thinking.
  

• Who Will Succeed: The individuals who most capitalize on the shift will be those who proactively rethink from first principles, starting now, and then keep iterating. Perhaps, the Ford of AI won't be found in the established tech giants—they'll emerge from unexpected places, armed with fresh perspectives and unencumbered by legacy thinking.
  

• The Compensation Revolution: Just as Ford had to double wages to retain assembly line workers, we'll see radical shifts in how AI-augmented work is valued and compensated.
  

• Compounding Gains: There won’t be one big insight that changes everything. The gains will come from stacking small insights over years.

The good news?

Unlike the Industrial Revolution, which required massive capital for factories and equipment, the AI revolution's primary requirement is mindset and methodology, for now at least. The tools are accessible. The compute is rentable. Right now, the core scarce resources are wisdom, courage, and time.

Thus, I've mapped out the five stages of AI mastery as a practical roadmap you can start today…

Your 10,000x Roadmap: The Five Stages of AI Mastery

The 10,000x knowledge worker will stack the gains from the following five stages I explain in this section:

1. Prompt Engineering (10x leverage)

2. Infinite Prompting (100x total) ← we are entering now

3. Model Management (1,000x total)

4. Model Leadership (10,000x total)

5. Autonomous Firms (unknown)

The Math Behind 10,000x Productivity

However, before I delve into each stage, it’s essential to understand that the 10,000x number is rough, but not arbitrary.

Let's do the math together.

First, what most people miss:

The gains from the five stages above compound on each other, they don't just add.

For example, when you can manage 10 AI agents simultaneously (Stage 3), each operating at 10x effectiveness, you're not 20x more productive—you're at 100x. When those agents can manage other agents (Stage 4), multiply again. The math isn't 10 + 10 + 10. It's 10 × 10 × 10.

Software development offers a preview of this coming economic reality. A March 2025 analysis from world-class programmer and thinker Steve Yegge, circulating among AI lab leaders, breaks down the stark economics:

The Current Cost Reality

Today's coding agents are expensive, consuming $10-12 per hour in LLM tokens—a sharp contrast to the $20 monthly subscriptions most AI users have today. As the analysis notes:

Each coding agent instance is approximately as valuable, amortized over this fiscal year, as having one additional junior level software developer on staff—provided that someone (human or AI) is keeping it mostly busy for 8-10 hours a day.

The math suggests developers should budget $80-100 daily for AI assistance—enough to run multiple agents while handling other work. This transforms a developer into a coordinator of AI workers rather than a solo contributor.

The Coming Wave: Agent Clusters

By Q3 2025, the analysis predicts a fundamental shift to"agent clusters"—developers running many agents in parallel:

This wave will enable each of your developers to run many agents at once in parallel, every agent working on a different task: bug fixing, issue refinement, new features, backlog grooming, deployments, documentation, literally anything a developer might do.

The New Economics of Development

The financial implications are profound. Running five agents simultaneously costs approximately $50 per hour—$100,000 annually per developer. Yet this investment yields 5x productivity gains.

Let's make this concrete. Today, a senior developer might:

• Write 100 lines of quality code per day

• Review 500 lines

• Participate in 2 hours of meetings

• Document part of a feature

With 5 agents running in parallel, that same developer becomes an orchestra conductor:

• 5,000 lines of code generated and tested

• 25,000 lines reviewed with AI-flagged issues

• Meetings transcribed, analyzed, and action items auto-assigned

• Complete documentation generated for all features

As the analysis concludes:

Software development is now a pay-to-play bullet train. If you can't afford a ticket, you risk getting red-shifted away from the pack.

Companies that can afford to build an AI team will accelerate dramatically. Those that don't won't just fall behind—they'll become fundamentally uncompetitive in a market where AI-augmented teams operate at 5-10x the velocity of traditional developers.

But here's the critical insight: throwing money at AI isn't enough—the winners will be those who master each stage of the AI productivity ladder, building skills that compound exponentially with each level.

Stage #1: Prompt Engineering
(Started 2022)

Since the advent of ChatGPT in 2022, the main way to create better outputs has been to create better prompts with relevant data.

The art evolved into a science as leading practitioners discovered the essential components of high-performance prompts:

• Chain of thought reasoning

• Relevant data

• Goals

• Roles

• Examples

• XML formatting

• Using variables

• Output Formats

• Etc

In addition, top-tier prompters also adopted practices pulled from software development, including:

• Building libraries of domain-specific instructions and data (second and third brains)

• Building evals

• Using version control

• A/B testing based on user segments

This systematic approach to prompt engineering transformed how early adopters extracted value from AI. By late 2023, the difference between a basic prompt and an expertly crafted one could mean the difference between generic, often inaccurate responses and highly specific, professional-grade outputs.

From there, the most successful prompt engineers discovered they could achieve 2-3x productivity gains by:

• Chaining prompts together so they can operate automatically in tandem

• Developing meta-prompting techniques where prompts could generate other prompts

• Mastering the art of data curation—feeding the model exactly the right context, examples, and background information at the right time

Yet even as these techniques matured, a ceiling emerged. No matter how perfect the prompt, you were still limited by the single-shot manual nature of the interaction. This constraint is beginning to be challenged by the next paradigm…

Stage #2: Infinite Prompting
(Started 2025)

We’re currently at the beginning of the age of synthetic reasoning. And here's what's actually happening right now as a result:

While most professionals are just starting to collect prompt templates, a small group has figured out how to make AI think deeply about their specific challenges. They're not just writing better prompts. They're orchestrating better thinking.

The gap between these groups is going to widen.

In a year, working with AI won’t primarily be about designing the perfect instructions for a single prompt, it will be more about creating “thinking structures” for AI so it can effectively think for hours and then days.

The best overview I’ve seen of what this reality will look like comes from a January 2025 Wall Street Journal interview with the founder and CEO of Anthropic, creator of Claude:

Source: WSJ

The thing we have in mind is an assistant in your workplace, an assistant that you use personally, but there's a model that is able to do anything on a computer screen that a kind of virtual human could do.

And you talk to it, you give it a task it does over like a day where, you know, you say, you know, we're going to implement this product feature. And what that means, it's writing some code, testing the code, deploying that code to some test surface, talking to coworkers, writing design docs, writing Google Docs, writing Slack, sending emails to people.

And just like a human, the model goes off and does a bunch of those things and then checks in with you every once in a while. So I would think of it as an agent. I would think of it as like an autonomous Virtual collaborator that, that acts on your behalf on a very long time scale and that you check in with every once in a while. You think of it as having all the piping, all the inputs and outputs of a human operating virtually.  

I'm not promising. I do suspect that a very strong version of these capabilities will come this year.

A revealing glimpse into this new paradigm also comes from an OpenAI senior engineer's frequent exchanges with Noam Brown, who heads OpenAI's inference thinking team. Whenever the engineer hit a roadblock, Brown's response was almost comically consistent:

“Why don’t we just let the model think for longer?”

This deceptively simple advice proved remarkably effective, pointing to a fundamental shift in how we'll work with AI:

As AI evolves, telling it exactly how to accomplish specific tasks will become a liability. This will happen in the same way that an incompetent manager telling a world-class technician how to do their job can lead to a worse outcome than simply supporting that person with whatever they need to do their job.

Mastery Levels Within Infinite Prompting

If you’re interested in understanding how to creating thinking structures for AI, I wrote the definitive article introducing Infinite Prompting, which I highly recommend:

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In the coming months, I will be writing about the mastery steps of Infinite Prompting once you’ve mastered the basics:

• Diverse Types: Understanding the various types of infinite prompts, including open-ended, divergent, and convergent.

• Full Application: Applying to more and more areas, including problem exploration, strategy, research, creation, editing, etc.

• Multi-Medium: Create infinite prompts that generate text, image, and/or video.

• Scaffolding: Understanding how to support AI thinking via various reasoning methods, perspectives, and evaluations in the ideal sequences.

• Meta-Infinite Prompting: Creating infinite prompts that create and execute infinite prompts in parallel, and then synthesize the results.

• Notifications: Setting up notifications so that when an infinite prompt is finished executing, it notifies you.

• Dashboard: Displays all prompts that are still executing, along with their estimated completion times.

Bottom line:

AI thinking time is the new currency of intellectual work. And those who learn to create it and spend it wisely will operate at a level of leverage we've never seen before.

Stage #3: Model Management (2025-2026)

In this next stage, top performers will be those who can most effectively toggle between and coordinate multiple AIs thinking in parallel over hours and days without becoming a bottleneck themselves.

The best explanation of what this future might look like comes from the same interview with Sholto Douglas that I clipped earlier:

Source: Unsupervised Learning Podcast

 There's this interesting transferal of—you are in the loop every second, to you are in the loop every minute, to you're in the loop every hour—that we've seen over the course of last year. And I wonder if it doesn't look like you're managing a fleet of models in future.

And so I think that kind of interface would be very interesting to explore. Just how much parallelism can you give someone when it's not a single model they're managing, but multiple models doing multiple things and interacting with each other?

I think that would be pretty exciting. I know a lot of people at Anthropic who have multiple Claude Code instances up in different dev boxes, which is pretty cool. But, I think that no one's really cracked that form factor yet.

And I think that's an interesting form factor to explore of what is the management bandwidth of an individual.

My other favorite example comes from Shopify CEO Tobi Lutke who built a team of agents to help him deliver a keynote address:

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At this stage, AI compute budget becomes a defining competitive advantage. Organizations that generate strong returns from AI can reinvest those profits into more computational power, creating a virtuous cycle—better AI capabilities drive superior outcomes, which fund even more advanced AI deployment. This flywheel effect will create an insurmountable gap between companies that harness it effectively and those that don't, fundamentally reshaping entire industries around those who master the economics of AI scaling.

Finally, the constraint on human management bandwidth will drive the next evolution: systems where AI models themselves become managers, creating hierarchical structures that mirror successful human organizations but operate at machine speed.

Stage #4: Model Leadership (2026-2027)

The next level is gaining the ability to manage models that manage other models. In the same interview, Sholto Douglas explains what this might look like:

Source: Unsupervised Learning Podcast

We initially will need humans to verify the outputs of these models. The economic impact of the models will be at some initial point bottlenecked by human management bandwidth, until you get to the point where you can delegate the trust in a model to itself manage teams of models.

That continual step up in hierarchy of abstraction layers will be one of the more important trend lines. Basically, based on the frequency with which you need to check these models, you become a gating factor. You have an infinite number of models running, and if you have to check them every 15 minutes versus every hour versus every five hours, you can do a lot more.

I think Jensen [Huang (founder and CEO of Nvidia)] mentioned this with respect to how he felt about the future of AGI and progress. He said, "Well, actually I am surrounded by a hundred thousand incredibly intelligent AGIs [humans]. This gives me huge leverage over the world and that sort of impact." He's describing how he himself is this gating factor in managing the company of Nvidia.

I think a lot of work ends up looking close to that direction. Who knows? Maybe this whole field of org design ends up being actually the most important. What is org structure, and how do you build trust? Org structure becomes complicated.

We get another glimpse into what it might be like to be a model leader from world-class computer programmer Steve Yegge:

The new job of a software developer going forward will soon be managing dashboards of coding agents and their AI supervisors... Some might derisively call this job babysitting... But we prefer to call it software development. This is our destiny.

The most successful AI orchestrators will be those who understand how to build trust hierarchies, delegate verification, and create self-improving systems that compound intelligence over time—essentially becoming CEOs of vast AI organizations.

Stage #5: The Rise Of Fully Automated Firms (2028-2030)

The emergence of fully automated firms will represent a phase change in economic organization that is hard to imagine. These companies will operate without human employees, make decisions in milliseconds, and evolve faster than any organization in history.

On the surface level, this might look amazing. We can spin up companies with tens of thousands of agents that generate revenue while we sleep. But, not so fast...

Source: X

Imagining this future is incredibly hard, but the best effort at it so far was published very recently.

Dwarkesh Patel—host of one of AI's most influential podcasts, where he interviews pioneers like Ilya Sutskever and Dario Amodei—recently published what may be the most realistic analysis of our economic future with this video essay, "What Will Automated Firms Look Like?"

The key insight is deceptively simple:

For the first time in history, you can just turn capital into compute and compute into labor.

This ability to directly convert financial resources into productive intelligence fundamentally breaks the constraints that have limited organizational growth since the dawn of commerce. As Patel notes:

Currently, firms are extremely bottlenecked in hiring and training people. But if your workers are AIs, then you can copy them millions of times with all their skills, judgment, and tacit knowledge intact.

In other words…

• No more hiring bottlenecks

• No more training delays

• No more knowledge loss when employees leave

Think about what this means. Today, if Apple wants to double its workforce, it needs years to recruit, train, and integrate thousands of people. Tomorrow, an automated firm could double its workforce in minutes, just by purchasing more computing power.

Meet Mega Steve: Your Future AI CEO

To understand how radical this shift will be, Patel introduces us to "Mega Steve"—an AI CEO inspired by Steve Jobs. But this comparison actually undersells the transformation.

Human CEOs, even visionaries like Jobs, see their companies through a tiny keyhole. They get filtered reports, attend select meetings, and review dashboards. They make decisions based on incomplete information because that's all any human brain can process.

Mega Steve is different. Imagine an AI executive that experiences every single thing happening in its company—simultaneously. Every customer service call in Tokyo. Every line of code written in Cupertino. Every supplier negotiation in Shenzhen. All of it, all at once, processed and understood in real-time.

But here's where it gets wild: Mega Steve doesn't just monitor—it acts through millions of specialized copies of itself. Need to debug code? Mega Steve spawns a thousand coding variants of itself. Negotiating with suppliers? Another thousand specialized negotiators appear. Each copy accumulates experience and knowledge that instantly flows back to the whole.

As Patel describes it:

You're going to have these blobs with millions of entities rapidly coming into and going out of existence who are each thinking at superhuman speeds.

The End of Talent Scarcity (As We Know It)

This changes the fundamental economics of talent. Today, great engineers, visionary designers, and brilliant strategists are rare and expensive. In an automated firm?

Want Steve Wozniak level engineering talent? Cool. Once you've got one, the marginal copy costs pennies. Need a thousand world-class researchers? Just spin them up.

The only constraint becomes compute power—and Moore's Law ensures that keeps getting cheaper.

From Humans to Hives

The communication advantage alone is staggering. Patel points out humanity's greatest limitation:

Biological brains don't allow information to be copy-pasted. So you need to spend years and in many cases decades teaching people what they need to know.

But AI workers?

They'll "communicate directly through latent representations"—sharing not just information but complete understanding instantly. No meetings. No misunderstandings. No knowledge lost when someone quits. Every insight, every bit of hard-won experience, perfectly preserved and instantly accessible across the entire organization.

This is why Patel calls it "a change in social organization as big as the transition from hunter-gatherer tribes to massive modern joint stock corporations."

The $100 Billion Question

Here's where business leaders need to pay attention. Patel asks: "Would it be worth it for Apple to spend $100 billion annually on inference compute for Mega Steve?"

His answer is an emphatic yes. That investment buys:

• Millions of subjective hours of strategic planning

• Complete analysis of every competitor move

• Perfect simulation of regulatory responses

• Exhaustive testing of every possible product iteration

Picture Mega Steve contemplating a major acquisition: "How would the Federal Trade Commission respond if we acquired eBay to challenge Amazon? Let me simulate the next 3 years of market dynamics. Ah, I see the likely outcome. I have 5 minutes of data center time left. Let me evaluate 1,000 alternative strategies."

That's millions of hours of analysis happening in minutes.

Rethinking our assumptions

When we reach fully automated firms, we’ll need to rethink our fundamental assumptions about what a company is, how it works, and its role in society:

The Infrastructure Is Already Here

The scaffolding for automated firms exists today and will only get better and cheaper:

• Digital Workforce Capabilities: Current AI models can already control computers, browse the web, write code, and interact with any software a human uses. They're learning to see through cameras and manipulate robotic systems.

• Financial Autonomy: AI agents now integrate with payment systems like Stripe, enabling them to purchase services, hire contractors, and manage budgets independently.

• Communication Channels: AI can already make phone calls, join video conferences, and send emails that are nearly indistinguishable from those of humans. An AI can now call your suppliers, negotiate with your customers, or pitch to your investors.

• Hierarchical Management: Most critically, AIs can now manage and coordinate other AIs, create specialized task configurations, and build complex workflows, laying the groundwork for the multi-agent systems these firms will require.

When I think about my field of thought leadership, it’s not hard to imagine a near-future AI newsletter company that autonomously:

• Identifies uncrowded niches with unmet information needs

• Creates a newsletter name and concept

• Registers a domain

• Sets up the newsletter with integrations to Stripe

• Creates a content calendar

• Publishes content on a regular basis

• Analyzes what worked and what didn’t

• Iterates

A New Form of Life

Patel's most profound insight might be his biological analogy. The gap between human and automated firms resembles "the gulf in complexity between prokaryotes and eukaryotes"—the evolutionary leap that made all complex life possible.

Just as eukaryotic cells enabled multicellular organisms through their ability to specialize and coordinate, automated firms will enable economic organisms of unprecedented complexity. We're not just building better companies. We're creating economic life forms that can evolve, adapt, and grow in ways we can barely imagine. It will learn faster than we do, never sleep, never forget, and can exist in millions of places at once.

Summary: The Five Stages of AI Mastery—From Prompter Engineer To Prompt Leader

These five stages represent the evolution from basic AI usage to orchestrating AI systems with 10,000x leverage:

Each level isn't just an incremental improvement—it's an order of magnitude leap in capability. A Prompt Engineer might save hours per week. An Infinite Prompter can compress months of work into days. A Model Manager operates like a small company. A Model Leader runs an intellectual empire.

We're already seeing the exponential pattern emerge in real-world coding:

Now that agents have emerged, we can start to see patterns. Each successive modality wave, beginning with chat, is conservatively about 5x as productive as the previous wave. Chat can be 5x as productive as manual coding, agents can be 5x as productive as chat, and so on. Note that each wave would probably grow to be 10x as productive as its predecessor, if left unchallenged and given time to mature. But they keep getting flattened by new, even faster modalities.
— Steve Yegge (world-class computer programmer and thought leader)

Most people are stuck at Level 1. The real game will be played at stages 2-5, where the leverage isn't 10x—it's 100x to 1,000x. Once we reach this level in the next few years, we will see single individuals create companies that make a billion dollars and impact a billion people. Astonishingly, the founder of Anthropic believes that this will happen in 2026:

Source: Anthropic Claude 4 Announcement

Finally, at stage #5, we enter a singularity that is hard to predict or prepare for. At that point, AI won’t just be a helpful assistant, it may be a superintelligent, autonomous, and agentic species writing the future of life on Earth.

In my opinion, we're not close to ready for this. History shows us what happens when a more capable form of organization emerges. And it doesn’t look good for the less capable life. If I could push a button, I would pause AI development until we figure out how to ensure safety. Every other challenge we face pales in comparison to the risks that AI could present us in just a few years.

The Weight of What's Coming: A Personal Reflection

Here’s what keeps me up at night…

On the one hand, I’m surfing an exhilarating wave that’s hard to stay on. I’m having to invent new ways to learn just for me to keep up. I love blowing people’s minds by showing them how they scale their impact, profit, and potential with AI beyond what they thought was possible. I think it’s important to educate people about AI so they can adapt as hundreds of millions of knowledge work jobs are disrupted in the next five years.

On the other hand, just as the potential of AI becomes clearer, so too do its risks. The same exponential curves that promise 10,000x productivity also threaten to:

• Blindside hundreds of millions of knowledge workers who are just learning prompting.

• Concentrate power in ways that make today's tech monopolies look quaint.

• Disorient everyone with AI-powered disinformation that could operate at scales that make today's fake news look primitive

• Shift geopolitics as nations with superior AI infrastructure could gain insurmountable advantages in economic competition, cyber warfare, and global influence.

• Make humans extinct as the acceleration of capabilities could outpace our ability to build safety measures, creating scenarios where powerful AI systems pursue goals misaligned with human values.

What’s most scary isn’t just the size of the change, it’s the speed. What would take centuries at today’s pace of change will happen in years.

In this strange hour, we’re somehow on the fence between watching our species birth its successor or unlock its own godhood. This is the burden of our generation—to surf the exponential curve while knowing it leads to a destination beyond all maps, where the only certainty is that nothing will ever be the same. We are not just living through history; we are living through the end of history as we've known it, and the beginning of something else entirely.

Personal Invitation: Ride The Wave With Me

Over the last two years, I’ve spent thousands of hours researching this AI knowledge work productivity revolution from the ground up.

Over the last ten years, I’ve taught thousands of students to learn faster, think better, and write blockbuster articles—all meta-skills that are profoundly helpful for interacting with AI.

Between these two experiences, I've learned something crucial: knowing the five stages of AI mastery is one thing—actually navigating them is another entirely.

That's why I recently launched the AI Thought Leader School.

It's an ongoing program where I share everything I've learned about how to utilize AI to learn faster, think more effectively, and write more clearly, ultimately becoming a recognized expert in your niche, generating a flood of new clients, and making a lasting impact with your ideas. Through 30 live courses across three interconnected tracks, we're building the exact skills that will matter as we progress through these five stages.

If you're ready to move beyond dabbling and start building real leverage in the AI age, join us.

Alternatively, if you simply want more in-depth articles like this one, along with $2,500+ in perks (7 on-demand AI classes and dozens of premium AI prompts), then I invite you to subscribe to this newsletter.