The wall is not size. It is structure. Every company runs on two kinds of data: state — the closed deal, the approved invoice — and reasoning — the Friday-night call where the discount got approved, the precedent the support engineer remembered, the off-label note. State is what Salesforce, Workday, and ServiceNow were built to store. Reasoning lives in Slack threads, Zoom calls, and the heads of senior people most likely to leave.

A context graph is the missing layer that captures it. Foundation Capital calls it AI’s trillion-dollar opportunity. Neo4j’s Will Lyon calls it “the missing why.” I’d go further: it is an organizational world model — a structured, living representation of how a company actually thinks.

The full essay traces why incumbents structurally cannot build it, what it means for B2B product managers, and where the moats will form. Read on →

World Model

The Million-Token Mirage

There is a strange double rhythm to artificial intelligence right now. On one beat, the industry celebrates ever-larger context windows — a million tokens, two, four — as though capability were simply a matter of wider doorways. On the other beat, the people actually pushing AI into production keep walking into a wall that wider doorways do not break. The disappointment is rarely about a model's reasoning. It is about what gets lost on the way in: the relationships between the pieces you dropped into the prompt…

Read more

7 days ago · 1 like · Cong

Previous Highlights

A salesperson approves a nonstandard discount on a Friday-night call. A doctor writes an off-label prescription with a one-line note. A support engineer escalates a ticket because she remembers the same bug from a different customer two years earlier. A general counsel agrees to an unusual indemnification clause because, last quarter, a similar one held up in arbitration. Each of these is a decision. Each is the kind of decision that makes a company itself. And almost none of them are durably captured by the software the company runs on.

This gap has, in the last six months, quietly acquired a name. A small but growing chorus of investors, founders, and database engineers has begun calling it the context graph: a structured, machine-readable record of an organization's reasoning, not just its outputs. Foundation Capital, in a widely circulated essay this spring, called context graphs "AI's trillion-dollar opportunity." Aaron Levie, the CEO of Box, debated the thesis on his podcast with Foundation's Joanne Chen and PlayerZero's Animesh Koratana and ended up agreeing — somewhat against his initial reflex. Will Lyon, a product manager at Neo4j, has taken to describing context graphs in the most deliberately plain way possible: "the missing why." In his definition, a context graph is "a knowledge graph that contains all of the information necessary to make decisions throughout the organization."

That definition is calm by design. It is also, I suspect, the most consequential idea in enterprise software right now.

To see why, consider the shape of the problem. Every company is sitting on two fundamentally different kinds of data. The first kind — the easy kind — is state: the closed deal, the approved invoice, the resolved ticket, the shipped order. State is what Salesforce, Workday, ServiceNow, SAP, and Snowflake were designed to preserve. The second kind — the hard kind — is the reasoning that produced that state. Why this customer got a forty-percent discount when the published floor was twenty. Why this account received custom payment terms. Why this candidate was hired despite a weak case round. Why this incident was triaged the way it was.

That reasoning, today, lives almost everywhere except in software. It lives in Slack threads, deal-desk calls, Zoom recordings no one has rewatched, two-line follow-up emails (”approved per our chat”), and the heads of the senior people most likely to leave. The moment those moments end, the reasoning evaporates, and the company is left with a state record that — to a future employee, or to a future model — is uninterpretable without the human chain of memory that produced it.

A model with a million-token window does not solve this. It just gives you a larger room in which to be confused. As Emil Eifrem, the CEO of Neo4j, has been putting it lately: the question is not whether you have enough information; it is whether the information has any structure to its relationships. Without that structure, longer context becomes, in his blunt phrase, a larger pool of noise.

What systems of record were never built to remember

The most useful frame I have heard for this comes from Aaron Levie, and it is, of all things, a story about travel agents.

Read more

The AI economy runs on a three-stage conversion chain: electricity becomes tokens, tokens become productivity. Each stage has its own economics, its own bottlenecks, and increasingly its own geopolitics. Understanding this chain is the difference between building on a foundation and building on a bet.

Stage one is electricity. This is where the most underappreciated structural advantage in AI sits today. In March 2026, President Trump told the heads of Amazon, Google, Microsoft, and OpenAI that any new AI data center in the United States must source its own power. The American grid cannot absorb the load. Meanwhile, China crossed 10 trillion kilowatt-hours of annual electricity consumption in 2025, more than double the US figure, built on seventy years of treating power as public infrastructure rather than a market commodity. Cheap, abundant electricity is the floor of any AI cost structure, and not every country has the same floor.

Stage two is the electron-to-token conversion. Two competing strategies have emerged. The American approach is hardware-led: own the best chips, own the cost curve. The Chinese approach is increasingly algorithm-led: squeeze more intelligence out of less silicon through architectural innovation. DeepSeek collapsed training costs through engineering. Kimi demonstrated that you can split inference across data centers running mixed-generation chips and still achieve competitive economics. These two curves are not in competition. They are complementary, and they will converge. The frontier of cost-efficient inference will belong to whoever combines both.

Stage three is the one nobody has solved. Tokens become productivity through human judgment, not through physics. A corporate lawyer can turn ten thousand tokens into a billion-dollar outcome. A student can spend the same tokens on an essay no one reads. There is no fixed exchange rate between tokens and dollars. The bottleneck is no longer the model. It is the human’s ability to decompose problems, design precise prompts, and route the right model to the right task. The most important capability of the next decade is not prompt engineering. It is structural thinking.

The market is already encoding this reality into its pricing. Jensen Huang’s three-axis token pricing framework (intelligence, speed, context length) is not marketing. It is a mirror of the underlying economics. The top tier will be priced like a luxury good. The bottom tier will commoditize toward zero. The dangerous place is the middle, where undifferentiated resellers of someone else’s tokens get squeezed from both directions.

The strategic question for every builder, investor, and operator: which conversion in this chain are you actually capturing? Infrastructure scale, algorithmic innovation, or the productivity layer where domain expertise determines value? Each is a viable position. But you have to know which one you occupy.

I break down the full analysis in the video above.

P.S. If this reframe is useful, share it with a founder or operator who is making infrastructure or model procurement decisions right now. And if you want the deeper playbook on how to apply this framework to your own product and career decisions, that’s what the paid tier of World Model is built for.

Previous highlights

The center of gravity in AI is shifting from model capability to applied economics. The decisive question of the next decade is not which lab releases the smartest system. It is who can transform raw electricity into useful intelligence at the lowest cost, with the highest reliability, at planetary scale. That is a question of infrastructure, geography, and policy, far more than it is a question of research.

A useful way to see this is to think of the AI economy as a three-stage conversion chain. At one end sits the electron, the cheapest input modern society produces. In the middle sits the token, the unit of account for everything large language models do. At the other end sits productivity, the actual economic output that users and enterprises pay for. The full chain is electricity → tokens → productivity.

Each stage has its own economics, its own bottlenecks, and increasingly its own geopolitics. The cost of converting electricity to tokens depends on chips, algorithms, and data center design. The cost of converting tokens to productivity depends on model quality, prompt design, and human judgment. The two conversions are governed by entirely different forces. Treating them as one continuous “AI cost” obscures more than it reveals.

This matters because the last decade of AI commentary has been dominated by a single mental model: scale wins. Add more parameters, more data, more compute, and capability follows. That model is not wrong, but it is incomplete. It explains how the field moved from GPT-2 to GPT-4. It does not explain what happens next.

What happens next is determined by the slope of three different cost curves: the cost of electricity, the cost of compute, and the cost of converting both into usable intelligence. Each curve has a different shape in different countries. Each is shaped by industrial policy choices made years or decades ago. And each creates a different kind of competitive advantage.

For a builder, this reframe has practical consequences. The choice of where to deploy, which model tier to buy from, and how to structure a workflow is no longer a purely technical decision. It is a bet on which conversion stage will compress fastest, in which jurisdiction. For a policymaker, it means that the AI race is not won at the chip foundry alone. It is also won, perhaps more decisively, at the substation.

The rest of this essay walks through the three stages in turn. It starts with electricity, where the most underappreciated story is unfolding in China. It moves to the electricity-to-token conversion, where the contest between hardware and algorithms is reshaping global cost structures. It ends with the tokens-to-productivity stage, where the pricing architecture of the AI economy is quietly being set in stone.

The Power Floor

In early March 2026, President Trump met with the heads of Amazon, Google, Microsoft, OpenAI, and three other technology companies. The meeting had a single agenda item: any new AI data center built in the United States would have to source its own power. Public grid capacity could not be drawn down for private model training.

That meeting captured something important. The constraint on American AI is no longer talent, capital, or even chips. It is electrons. AI workloads are projected to consume a rising share of US grid capacity over the next several years, and the grid is not expanding fast enough to absorb them. The result is a slow, structural ceiling on how cheaply American AI can be produced.

The same story is playing out across most advanced economies. German power prices spiked to a yearly high. India is rationing electricity to industry. Spain and Portugal recently lived through the largest blackout in their modern history. The energy crunch is not a temporary supply shock. It is the predictable consequence of treating electricity as a market commodity in countries that did not invest enough in long-cycle infrastructure.

Then there is China. In 2025, total Chinese electricity consumption crossed 10 trillion kilowatt-hours, a threshold no country had previously reached. That figure is more than double American consumption. It exceeds the combined annual usage of the European Union, Russia, India, and Japan. The Financial Times described the moment as the arrival of “the first electricity empire in human history.”

This did not happen by accident. From the founding of the People’s Republic, electricity was treated not as a commodity but as public infrastructure, on the same legal footing as roads and water. That framing produced seventy years of consistent state-led investment, regardless of the political and economic cycle. The Three Gorges Dam, an idea first sketched by Sun Yat-sen in 1919, eventually became operational. The West-to-East Power Transmission Project, which moves electricity from the energy-rich interior to the demand-rich coast, became one of the largest infrastructure systems on earth.

The Three Gorges Project is one of the superprojects with the most significant overall benefits

Read more

Previous highlights

I thought about those years constantly when Granola started showing up in my workflow in 2024. The pattern was inverse. It asked for nothing. I opened my laptop, I joined a Zoom or a Google Meet or a Teams call, and Granola just sat there, doing its thing, invisible to everyone else on the call. No wake word. No bot avatar in the participant list. No link to paste into the chat. I didn’t have to tell anyone I was using it. I didn’t have to tell myself. It took me a few weeks to realize that this was the entire product, and that the entire product was also the entire strategy.

Last month Granola closed a $125 million Series C at a $1.5 billion valuation, led by Danny Rimer at Index Ventures and joined by Mamoon Hamid at Kleiner Perkins. Reported revenue growth in the preceding quarter was 250 percent. The cap table now includes Tobi Lütke, Amjad Masad, Karri Saarinen, and Guillermo Rauch, which is an unusually dense concentration of product-led founders voting with their own money. What I want to write about here is why I think any of this happened, and what any of us building AI products should actually take from it.

The Year They Cut Half the Product

Granola was incorporated in London in March 2023 by Christopher Pedregal and Sam Stephenson. Pedregal had founded Socratic, an AI tutor acquired by Google, and then built Stack inside Google’s Area 120 incubator before it was folded into Google Drive. Stephenson was a designer whose career ran through a digital ski coach, a swim coaching wearable, and a low-friction note-taking app called Ideaflow. Neither of them was a first-time founder. Neither of them was in a hurry.

Read more

Previous Highlights

Subscribe now

Here is a confession that will make me sound like a Luddite: the most valuable product insight I ever received came from watching a 58-year-old woman hold a phone.

Not from a dashboard. Not from an A/B test. Not from a Mixpanel funnel. From sitting in a windowless room in Shenzhen, behind a one-way mirror, watching her try to find the camera app.

She tapped the screen with her index finger, just one, like she was pressing a doorbell. She squinted. She rotated the phone 90 degrees, then back. When the moderator asked her what she thought of the 200-megapixel camera, she said: “I don’t know what that means. I just want the photos to look like what I see.”

That single sentence killed a feature that three teams had spent months building. And it saved us from shipping a product that solved a problem nobody had.

This is a story about focus groups, the oldest, least glamorous, most unfashionable tool in the product manager’s toolkit. And about why, paradoxically, they matter more now than they have in twenty years.

The Paradox of the Agent Era

We are living through the most capability-rich moment in the history of software. AI agents can now browse the web, write code, query databases, draft reports, and chain these actions together in multi-step workflows. The gap between “what is technically possible” and “what we can ship” has never been wider.

And that gap is precisely the problem.

When capability is cheap, taste becomes expensive. When you can build almost anything, the hardest question is no longer how but what, and more importantly, for whom. I wrote previously about how great products don’t change user behavior; they embed into existing muscle memory. WhatsApp didn’t teach people a new way to communicate. It digitized what they were already doing. The products that fail are the ones that ask users to develop new habits for the privilege of using them.

In the agent era, this principle is even more critical. Every founder I talk to right now is building some variation of “AI that does X for you.” The technical capability is real. But the user understanding is often paper-thin. They are building agents that automate workflows they’ve never actually watched a human perform.

This is where focus groups come in. Not as a relic from the Mad Men era, but as the specific antidote to the specific disease of 2026: building powerful tools for imaginary users.

Why Online Surveys Are Lying to You

I can already hear the objection. “We do user research. We run surveys. We have data.”

Let me tell you what your online survey data actually contains: noise. A tremendous, statistically impressive quantity of noise.

Here is what happens when you send a survey to 500 people. Some of them are multitasking. Some are answering on the toilet. A meaningful percentage are satisficing, clicking whatever answer gets them to the end fastest. The ones who do engage thoughtfully are self-selecting in ways that systematically bias your sample.

But the deeper problem isn’t data quality. It’s data type. A survey can tell you what people say they want. It cannot tell you why they want it. It cannot capture the moment of hesitation, the furrowed brow, the half-sentence they start and then abandon. It cannot show you the 58-year-old woman pressing the screen like a doorbell.

Surveys are closed-loop instruments. You define the questions, you constrain the answers, and you get back a reflection of your own assumptions. If you’re a founder trying to find your blind spots, a survey is like looking for your car keys under the streetlight because that’s where the light is.

A focus group is the opposite. It is an open-loop instrument. You put four or five real humans in a room, you ask them a question, and then you shut up. You let the conversation go somewhere you didn’t expect. You watch their faces. You hear the specific language they use to describe their problems, language that is almost never the language you use internally.

This is the part that cannot be automated and cannot be surveyed. And it is the part that separates products that ship features from products that solve problems.

How to Assemble the Room: Selecting Your Focus Groups

The rest of this article, including my exact framework for selecting focus group participants, the questioning technique I developed, and the feature prioritization method that consistently surfaces your product’s “wow factor”, is available to paid subscribers.

This is the first piece in The AI PM Playbook series. Upcoming installments will cover pricing research, competitive teardowns, and how to write product specs in the age of agents.

Read more

Previous highlights

In a previous post, I covered what OpenClaw is and why Tencent is going all-in on it with eight new enterprise agents. Today, I want to go deeper. I want to crack open the lobster and show you what's actually inside—not at the code level, but at the conceptual level. Because understanding how these agents work under the hood is the difference between using one effectively and watching it delete all your emails.

The Lobster Is Not the Brain

Here is the single most important thing to understand about OpenClaw, and honestly, about every AI agent you’ll encounter in 2025 and beyond: OpenClaw is not artificial intelligence.

OpenClaw is not a language model. It doesn’t think. It doesn’t reason. It doesn’t understand your commands. It is, as one researcher put it, a crustacean—a simple creature running on hardcoded rules, sitting between you and the actual AI.

The architecture is straightforward. You send a message to OpenClaw through WhatsApp, Telegram, or a web interface. OpenClaw takes your message, wraps it in a massive block of context—who the agent is, what tools it can use, what it remembers about you—and passes that entire package to a cloud-based language model like Claude, GPT, or Gemini. The language model does the actual thinking. It generates a response. OpenClaw receives that response, checks if the model wants to use any tools, executes those tools on your computer, and sends the result back to you.

That’s it. OpenClaw is a middleman. A very sophisticated, very useful middleman.

Why does this matter? Because the intelligence of your agent depends entirely on which language model you plug into the back end. The professor discovered this the hard way. When he first set up his lobster, he connected it to a cheaper, older model. The result was abysmal. The agent couldn’t complete basic tasks, got confused constantly, and felt like a toy. He was ready to dismiss the entire concept. Then he switched to a newer, more capable model, and the difference was night and day. Suddenly the agent could plan multi-step workflows, write code, navigate web pages, and manage files with genuine competence.

Same lobster. Same software. Completely different brain.

This is the first decision you need to get right if you’re considering putting Claw into your workflow: the model you choose is the agent you get.

How It Fakes Having a Personality

If OpenClaw is just a dumb relay between you and a language model, how does it feel so much like a personal assistant? How does it remember your name, your preferences, your ongoing projects?

Read more

Subscribe now

Today I’m announcing a brand new addition to my Substack publication: World Model subscriber chat.

This is a conversation space exclusively for subscribers—kind of…

Read more

I turned my recent post “Hands-on Mercor Playbook” into a short video remix—hope you enjoy it.

Previous highlights

So here it is. The playbook I wish someone had handed me on day one.

But first, a mindset shift. Because if you walk into Mercor thinking like a traditional freelancer—or worse, like a salaried employee—you’re going to have a terrible time.

The AI-Era Uber

Let me give you the mental model that changed everything for me.

Mercor is Uber for AI training. Structurally.

Think about what Uber did. It sat between people who needed rides and people who could drive. It didn’t employ the drivers. It didn’t own the cars. It built the marketplace, handled the matching, and took a cut. The drivers showed up, completed trips, got rated, and either kept getting rides or didn’t.

Mercor does the same thing, except the “riders” are frontier AI labs—OpenAI, Google DeepMind, Anthropic, Meta, and others building the most powerful models on the planet—and the “drivers” are domain experts like you and me. The labs need human-generated data to train and evaluate their models. They need people who can spot where GPT-5 or Gemini or Claude falls apart, write better responses, and judge which outputs are actually correct. Mercor finds those people, organizes them into project teams, and manages the whole operation.

Once you internalize this, a lot of things that frustrate people suddenly make sense.

Projects ending abruptly? That’s normal. The AI lab finished that phase of training, or their evaluation goals shifted, or the model got good enough at that particular task that they no longer need human data for it. It’s not personal. It’s not a performance issue. It’s a ride ending because the passenger reached their destination.

I see people in community groups losing their minds every time a project gets terminated. “I just got off-boarded!” “They cut us without warning!” I understand the anxiety. Losing income is stressful. But panicking about project termination on Mercor is like an Uber driver panicking every time a passenger gets out of the car. That’s… how it works.

The better analogy might be commercial fishing. You go where the fish are. Some seasons are abundant. Some aren’t. You maintain your gear, keep your skills sharp, and stay ready for the next run. The fisherman who prospers isn’t the one who catches one big haul and retires. It’s the one who shows up consistently, reads the water, and adapts.

So rule number one of the Mercor playbook: get comfortable with impermanence. Don’t over-index on any single project. Don’t mentally spend money you haven’t earned yet. And don’t mistake a project ending for a career ending.

What You're Actually Getting Paid For

Here’s something that trips up a lot of new Mercor contributors, especially people coming from traditional tech jobs: you’re not getting paid to be smart. You’re getting paid to find where the model is stupid.

That distinction matters enormously. The AI labs aren’t hiring domain experts because they want you to demonstrate your knowledge. They’re hiring you because their model needs to get better, and the only way it gets better is if humans identify the gaps and fill them with high-quality data.

Your job, at its core, is quality control and gap detection. You’re looking at model outputs and asking: Where did this go wrong? What did it miss? What does it think it knows but actually doesn’t? And then you’re writing responses that show the model—or, more precisely, the training pipeline—what a correct, complete, expert-level answer actually looks like.

This requires a specific kind of thinking. You need to resist the urge to just “write a good answer.” Instead, you need to interrogate the model’s attempt first. Find the failure modes. Spot the hallucinations, the subtle logical errors, the places where the model sounds confident but is actually making things up. Then, and only then, craft the expert response that addresses those exact weaknesses.

The people who do this well become invaluable. The people who just bang out competent answers without really engaging with why the model failed get average ratings and wonder why they’re not getting staffed on the good projects.

The SPL: Your Most Important Relationship

Every Mercor project has an SPL—a Special Project Lead. If Mercor is Uber, the SPL is the dispatcher who also happens to be your local manager, client liaison, and quality gatekeeper rolled into one.

The SPL sits between you and the AI lab client. They translate the client’s requirements into actionable guidelines for the writing team. They communicate quality expectations. They handle escalations. They’re the reason you don’t have to deal directly with a Google DeepMind product manager who has very specific ideas about what “expert-level reasoning” looks like in organic chemistry responses.

Here’s what I learned about working with SPLs: treat this relationship like you’d treat a relationship with the best manager you’ve ever had. Communicate proactively. Ask smart questions early. Don’t wait until you’ve submitted 50 tasks to find out you’ve been interpreting the guidelines wrong. If something in the project playbook is ambiguous—and something always is—raise it immediately.

The SPLs I’ve worked with have been sharp, responsive, and genuinely invested in their contributors’ success. They want you to do well because your quality scores reflect on their project. This alignment of incentives is a gift. Use it.

But here’s the flip side: SPLs are also the people who notice when you’re not meeting requirements. They track your quality scores, your throughput, and your adherence to guidelines. If you’re consistently missing the mark, they’re the ones who’ll have to bench you. So the relationship cuts both ways. Earn their trust early, maintain it consistently, and you’ll find yourself getting first pick of tasks and early access to new projects.

Respect the AHT (Or Pay the Price)

Let’s talk about the metric that governs your life on Mercor: AHT, or Average Handling Time.

Read more

I turned my recent post “OpenClaw” into a short video remix—hope you enjoy it.

Previous highlights

His paper, “A Mathematical Theory of Communication,” turned out to be the entire foundation of the digital age.

I keep thinking about Shannon whenever I read the headlines about OpenClaw. Last week at GTC 2026, Jensen Huang stood on stage in his leather jacket and called OpenClaw “the operating system of agentic computers.” He said every company in the world needs an OpenClaw strategy, the way they once needed a Linux strategy, an Internet strategy, a mobile strategy. The crowd went wild. NVIDIA announced NemoClaw, an enterprise-ready reference stack. Peter Steinberger, the creator, was there in person — having already been hired by OpenAI to steward the project into a foundation.

And in the flood of analysis that followed — the security breakdowns, the GTC recaps, the breathless “this changes everything” takes — almost everyone missed the same thing.

The most important part of OpenClaw has nothing to do with AI.

What OpenClaw Actually Is

Let me cut through every buzzword you’ve seen this month and tell you what OpenClaw actually is, architecturally.

Read more

Previous highlights

In the Industrial Age, the unit of power was the Watt. In the Information Age, it was the Bit. In the AI Age, the Token is the fundamental unit of intelligence. At GTC 2026, NVIDIA CEO Jensen Huang stood before a crowd and didn’t just talk about chips; he talked about “Token Factories.” In Jensen’s vision, the massive data centers of the future are no longer just storage units—they are manufacturing plants that consume electricity and output “Intelligence” in the form of tokens.

“Token King“ Jenson Huang at GTC 2026

The Tokenizer: How Machines Digest Humanity

To understand a token, we have to look at the Tokenizer. Think of this as a translator. AI doesn’t see words; it sees numbers.

• Independent Vocabularies: Every model (GPT, Claude, Llama) has a “dictionary” set in stone before it ever starts learning. One model might have a 32,000-word dictionary; another might have 100,000. These are entirely custom-built by each company.

• The Embedding (Semantic Coordinates): This is the soul of the machine. The model turns a word into a “high-dimensional vector”—basically a set of coordinates in a massive digital ocean of meaning. Different models “map” the world differently. In Model A’s mind, “Apple” might be closest to “Fruit.” In Model B’s mind (perhaps trained more on tech news), “Apple” might be closer to “Huawei” or “NVIDIA.”

Not All Token are Created Equal

The biggest mistake people make is assuming a "Token" is a standard unit like an "Inch" or a "Liter." It’s not. We are currently in what I call the "Shambolic Stage" of AI measurement.

Read more

If you enjoy World Model, it would mean the world to me if you invited friends to subscribe and read with us. If you refer friends, you will receive benefits that give you special access to World Model.

How to participate

1. Share World Model. When you use the referral lin…

Read more

There is a new category of jobs quietly emerging in the tech world. It doesn’t require you to live in Silicon Valley, and it doesn’t require a traditional corporate resume.

What it does require is deep domain expertise and the ability to teach artificial intelligence how to think.

Over the last four months, I’ve made $37,000 working on Mercor, a platform that connects experts with top-tier AI labs. I am currently a top performer at one of the leading AI training data companies and rank in the top 10% of earners.

This is my first article breaking down exactly how this ecosystem works—and more importantly, how you can break into it.

The Shift: The Demand for Human Intelligence is Real

Right now, Google, OpenAI, Anthropic, and others are locked in an arms race. But they’ve hit a bottleneck: they are running out of high-quality human data to train their models.

To bridge the gap from GPT-4 to the next frontier, these companies need experts for SFT (Supervised Fine-Tuning) and RLVR (Reinforcement Learning from Verifiable Rewards). They need humans who can evaluate complex agentic workflows and correct AI when it hallucinates.

The demand for human experts is massive, and the compensation reflects that.

The First Hurdle: Cracking the AI Interview

To get access to these high-paying tasks, you have to pass the vetting process. Here is where most people fail: Your interviewer is an AI.

Read more