essay_the_judgment

Imagine if an Olympic gymnast could coach 50 million young athletes at once.

Not deliver a motivational speech. Not appear in a training video. Not lend her name to a product.

Coach them.

Watch their routines. Diagnose what needs work. Adapt the feedback to each athlete’s progress, confidence, and skill level.

In the physical world, that’s impossible. But it’s the future that Nichole Francis Reynolds, a friend of mine, is working to make real.

Francis Reynolds is the founder of Habitude Sports, an AI-powered training and wellness platform for gymnastics. A lawyer and tech policy professional by background, she’s the mother of two young athletes: a baseball player and a Junior Olympic gymnast.

Habitude uses agentic AI and AI-driven video analysis to break down gymnasts’ skills and routines, support scoring, track progress, teach rules, and reinforce daily training and wellness habits.

"Well-being isn’t a side feature here,” says Francis Reynolds. “It’s a core pillar, on equal footing with performance.”

The company is also building a network of elite gymnasts who serve as virtual coaches and brand ambassadors. They include former Olympic all-around champion Carly Patterson, along with US national team alumni Brody Malone, Ian Gunther, Margzetta Frazier, and Paul Juda.

That’s where the idea gets bigger.

Traditionally, sports training platforms like Peloton and Nike Training Club have used elite athletes to produce training content. The athlete records a workout, demonstrates a technique, shares advice, and reaches a large audience. Everyone receives essentially the same instruction.

Habitude Sports is trying to do something different. It uses AI to scale personalized judgment.

Instructional videos can show young athletes what excellence looks like. Judgment tells them what's wrong, what matters, and what to do next.

Each gymnast records a routine and uploads it to the platform. The system identifies strengths and weaknesses, connects feedback to her progress over time, and reinforces the physical and mental habits she needs to improve.

Habitude’s initial market is the estimated 50 million people worldwide who practice gymnastics at all levels of the sport. But the same model applies to any field where performance depends on trained perception: tennis, dance, music, sales coaching, clinical training, leadership development, and beyond.

The value is no longer just access to an athlete’s image, personal story, or instruction. It is access to expert, personalized judgment at global scale.

What experts see

To understand why that matters, it helps to distinguish information, expertise, and judgment.

Information tells us what is there: the routine, the score, the price, the metric, the data point.

Expertise tells us what it means. A great coach, surgeon, musician, trader, editor, athlete, or engineer sees patterns others miss. Some of that knowledge can be explained. Some of it is tacit: real, practical, and visible in action, even when it cannot be fully reduced to rules.

Judgment is expertise applied to a choice. It is the ability to know what matters now, what can wait, what is noise, and what to do next.

Information tells us what is there. Expertise tells us what it means. Judgment tells us what matters now.

In 1966, the Hungarian-British philosopher Michael Polanyi gave this concept a useful name: tacit knowledge. Some knowledge is real and practical even when it can’t be fully expressed as rules, instructions, or data. In Polanyi’s now-famous phrase: “We can know more than we can tell.”

What separates a skilled gymnastics coach from a less experienced one is not simply a larger library of drills. She sees the athlete differently. She notices the hesitation before a skill, the shoulder angle, the timing problem, the landing pattern, the fear hidden inside a technical mistake. She knows which correction matters now and which can wait.

This is why AI changes the economics of expertise. If AI simply produces more content, it deepens a problem we already have: too much output, not enough understanding. But if AI can help capture, refine, and apply expert judgment, the business model becomes more interesting.

The scarce resource is no longer the lesson, the video, the article, or the dataset. It’s the trained capacity to interpret what the information means.

What markets see

This is one way to understand the emerging judgment economy. The term is often used to describe a world in which AI makes production and analysis cheaper, while human discernment becomes more valuable.

But judgment is not simply a faculty that individuals exercise after AI produces an output. It’s also something markets do collectively. Customers, investors, analysts, journalists, employees, and partners are constantly judging what companies are, why they matter, and what evidence should count.

Customers don’t need more claims. Investors don’t need more positioning. Analysts don’t need more decks. They need better ways to judge what matters.

Judgment is expertise applied to a choice.

The French anthropologist Pierre Bourdieu used the term "habitus" to describe the durable dispositions people acquire through experience: ways of seeing, classifying, valuing, moving, and responding that become second nature.

Markets develop something similar. Investors, analysts, customers, journalists, and partners learn to classify companies through familiar categories. Over time, those categories start to feel natural.

AI company. SaaS vendor. Service provider. Infrastructure company. Media platform. Marketplace. System of record.

These aren’t neutral categories. They shape judgment before analysis begins.

A company framed as training content will be judged by the standards of content: audience, engagement, talent, and distribution. A company framed as coaching infrastructure invites a different set of questions: personalization, performance improvement, retention, and scale.

The category changes the test. It tells the market what evidence to look for, what risks to worry about, and what kind of value to reward.

This is why framing matters. A frame is not a tagline or a category label. It’s a learned way of seeing. Companies with real authority don’t just explain themselves better. They teach the market how to judge them.

Claiming the frame means changing the market habitus, not just adding a new label.

Bloomberg and financial judgment

The judgment economy predates generative AI.

The Bloomberg Terminal is often described as a financial data product. That’s true, but incomplete. Its value has never been only that it provides information. Financial markets are saturated with prices, filings, news, charts, models, research, and commentary.

Bloomberg became essential because it embedded data inside the working judgment of finance professionals. It gave traders, analysts, portfolio managers, and bankers a common environment for seeing markets, comparing instruments, monitoring signals, testing assumptions, communicating with peers, and acting quickly.

If the evidence is strong enough, a new way of seeing becomes common sense.

Bloomberg also became a social environment. Its messaging tools helped finance professionals trade market color, compare notes, and talk to one another inside the same system they used to monitor markets.

That matters because judgment is not formed in isolation. It circulates through professional communities. The Bloomberg Terminal didn’t just deliver more information. It shaped the way a profession perceived, discussed, and navigated its field.

That is a deeper form of authority. Competitors can offer cheaper data, cleaner interfaces, or similar features. But once a tool becomes part of how a community sees and talks, competitors are also competing against an installed structure of professional perception.

That’s how the Bloomberg Terminal became part of the daily machinery through which modern finance professionals form, test, and share judgment.

AI factories

NVIDIA is attempting a similar move in AI infrastructure.
The obvious story is that NVIDIA sells chips. A broader story is that it sells accelerated computing systems. But the company’s most ambitious frame is the “AI factory.”

That phrase asks customers, partners, and investors to stop seeing data centers as places where information is stored or processed. Instead, they should see them as production systems that manufacture intelligence at scale.

That changes the tests the market applies.

If NVIDIA is a chip company, the relevant questions are about semiconductor performance, supply constraints, margins, and product cycles. If NVIDIA is the infrastructure layer for AI factories, the market begins to ask different questions: How much intelligence can be produced per watt? How efficiently can models be trained, fine-tuned, and deployed? How deeply is the ecosystem integrated? How strategically dependent are customers on the architecture?

The frame changes the judgment.

This is not simply a messaging issue. A company cannot claim an infrastructure frame unless the business can support the tests implied by the story. The stronger the frame, the more demanding the evidence must be.

So the point is not that “AI factory” is a clever phrase. The point is that it teaches the market what to see.

Authority is trained perception

These three examples differ in obvious ways, but the underlying pattern is similar. Habitude Sports applies expert judgment to individual athletic performance. Bloomberg embeds financial information inside the daily judgment of a professional community. NVIDIA’s AI factory frame teaches the market to judge AI infrastructure by a different set of tests.

Authority is often treated as a communications asset: a stronger message, a clearer narrative, a better point of view. Those things matter, but they are not the end state. Real authority accrues when the market adopts your way of seeing.

That happens through repeated proof.

Evidence makes a new frame usable, credible, and eventually obvious. Research, benchmarks, customer outcomes, operating metrics, technical proof, adoption patterns, and field intelligence all help audiences distinguish signal from noise.

Proof teaches the market how to see. Valuation follows only when the new way of seeing becomes believable.

The new scarcity

In the AI era, intelligence is becoming ambient, embedded, and cheap to invoke. The urgent question is what remains scarce when intelligent-seeming output is everywhere.

The answer is not simply better content. AI is driving the cost of fluent explanation and polished creative toward zero. In this environment, scarcity moves elsewhere: toward trust, proof, and judgment.

These are related concepts. Trust depends on proof. Proof guides judgment. Judgment determines what the market believes, funds, and rewards.

In the judgment economy, the winners won’t be the companies that produce the most information or the most compelling content. Instead, winning companies will teach the field what to notice, what to value, and eventually what to take for granted.