The Hidden Variable in T2V Velocity: Realization Probability

A Precisely Captured Fundamental Contradiction

Sean's T2V Velocity concept precisely captures the fundamental contradiction in AI commercialization. Using real-world data from two businesses, he revealed a truth the entire industry has been ignoring.

The Deeper Value of This Metric

The T2V formula is essentially measuring value verification velocity.

Traditional SaaS looks at Time-to-Value, but in the AI era, the cost structure is completely different—tokens are explicit, measurable marginal costs. This makes the "input-output-verification" loop trackable with precision for the first time.

I particularly agree with Sean's insight that "customers don't pay for tokens; they pay for closed loops." This explains why:

ChatGPT Plus has high user retention (daily conversation loops close fast)
Enterprise AI coding tools have high churn (value verification cycles are too long)
Marketing AI tools monetize most easily (ROI is immediately visible)

Adding a Dimension: Realization Probability

Sean's formula might be missing one variable—realization probability.

T2V Velocity = (Business Value × Realization Probability) / (Token Cost × Days to Close Loop)

Why? Consider this comparison:

Scenario	Tokens	Result	Realization Probability
Short Video Script	200	Publish → Data guaranteed in 2 hours	100%
Code Refactoring	100,000	Deploy → Might see no effect at all	30%?

After 200 tokens, short videos are 100% guaranteed to have data (quality aside), while code refactoring with 100k tokens might only have a 30% chance of visible improvement.

Programmers aren't unwilling to pay; they're uncertain what 100k tokens will ultimately deliver.

This uncertainty exponentially amplifies the negative impact of long loop cycles.

Theoretical Foundation

"Realization probability" isn't something I invented from thin air—it's derived from several established concepts:

1. Expected Value Theory

A classic economics concept: Expected Value = Actual Value × Realization Probability.

Nobel laureate Daniel Kahneman's Prospect Theory is built on this foundation—people consider both the magnitude of gains and the probability of realization when making decisions.

2. Risk-Adjusted Return in Finance

Sharpe Ratio = (Investment Return - Risk-Free Return) / Standard Deviation

Finance has long established: uncertainty discounts value. Between two investments with the same expected return, the one with lower volatility (higher certainty) is worth more.

3. Implicit Concepts in SaaS

SaaS actually has related metrics, but they haven't been explicitly named or quantified:

Realized Value vs Promised Value: Customer success teams track "how much of the promised value was actually realized"
Value Realization Rate: Platforms like Gainsight have this dimension, but mainly for internal analysis
The Logic Behind NPS: Net Promoter Score reflects, to some degree, customers' certainty about "continuing to receive value"

But indeed, no standard formula directly incorporates this. So this is a reasonable derivation based on reliable theory, not a standard term.

Common Characteristics of High T2V Scenarios

Re-examining through the T2V framework, high T2V scenarios share four characteristics:

Decision-making power lies with the executor (short video operators decide whether to post)
Value can be quantified (views, conversion rates, sales)
Low cost of trial and error (if one script doesn't work, try the next)
Short feedback loops (hours, not months)

Deepractice's decision to pivot from AI programming now appears correct.

The T2V Formula Already Implicitly Contains This Dimension

Looking back at Sean's original data:

Scenario	Business Value	Reality
Viral Short Video	$1,400	Occasionally viral, mostly just hundreds
Code Refactoring	$2,800	Might not show any effect at all

If we adjust with expected value:

Short Video: $1,400 × 10% (viral probability) = $140 expected value
Code Refactoring: $2,800 × 30% (visible value probability) = $840 expected value

Calculated this way, the T2V gap shrinks, but short videos still lead—because loops close fast + low trial-and-error cost.

Conclusion on the Variable

My suggested "realization probability" variable:

✅ Has theoretical foundation: Expected Value Theory, Risk-Adjusted Return
✅ Has practical support: SaaS customer success management implicitly includes this dimension
❌ Not standard terminology: This is a derivation based on established theory

Sean's original formula is already powerful enough. If adding this dimension, I suggest:

Either explicitly state it's "an extension based on Expected Value Theory"
Or keep the original formula simple and mention "uncertainty of value realization" as qualitative analysis

As the proposer of a new concept, over-complicating the formula might weaken its spreadability.

Sean's original insight—"loop velocity matters more than token consumption"—is already paradigm-shifting enough.

The Deepest Strategic Meaning of T2V: The Data Flywheel

Why does loop velocity matter more than token consumption?

Because it's not about who consumes more tokens being able to leverage more data. Data is the most important asset for any individual or company in the AI era.

T2V reveals a more fundamental issue:

Surface level: Token → Business Value (one-time transaction)
Deep level: Token → Data Accumulation → Sustainable Competitiveness (compound effect)

Re-examining Sean's two businesses:

The Data Flywheel in Short Video

graph LR
    A[200 Tokens] --> B[5 Scripts]
    B --> C[Publish]
    C --> D[Data in 2 Hours]
    D --> E[Optimize Strategy]
    E --> A

Day 1: 200 tokens → 5 scripts → 5 data points
Day 2: 200 tokens → Optimize based on yesterday's data → Another 5 data points
Day 30: 150 accumulated data points → Already know which scripts work for which audiences at which times

150 validations accumulated in 30 days.

The Data Dilemma in Programming

graph LR
    A[100k Tokens] --> B[Develop Feature]
    B --> C[Test & Integrate]
    C --> D[Wait for Deploy]
    D --> E[Wait for Feedback]
    E -.-> F[Data in 30 Days]
    F -.-> A

Month 1: 100k tokens → Develop Feature A → Wait for deployment
Month 2: 100k tokens → Continue development → Still waiting for feedback
Month 3: Finally have data → But may have missed the adjustment window

Only 1 validation completed in 3 months.

Data accumulation speed gap: 450x.

This Explains a Counter-Intuitive Phenomenon of the AI Era

Traditional thinking: Big companies have the advantage because they can burn more tokens
AI era truth: Whoever closes loops faster spins the data flywheel faster, and builds real moats

This is also why OpenAI's ChatGPT (fast loops) has been more successful than Codex (slow loops) at the fundamental level.

The Compound Effect of Data Assets

Sean's insight gives T2V a second layer of meaning:

T2V 1.0: Token-to-Value (Short-term ROI)

Customer perspective: How quickly can I earn back the tokens I spent?

T2V 2.0: Token-to-Data-Velocity (Long-term Moat)

Company perspective: How quickly can my token spend convert to data assets?

Key formula:

Data Asset Growth Rate = (Effective Validations / Day) × Data Quality

Short Video: 5/day × High quality (direct feedback) = Rapidly build "viral formula"
Programming: 0.03/day × Low quality (indirect feedback) = Slow experience accumulation

Conclusion: The Three Layers of T2V Value

graph TB
    subgraph Strategic
        C[Data Asset Velocity]
    end
    subgraph Revenue
        B[Value Monetization Speed]
    end
    subgraph Cost
        A[Token Efficiency]
    end

    A --> B --> C

Surface layer: Token efficiency (cost perspective)
Middle layer: Value monetization velocity (revenue perspective)
Deep layer: Data asset accumulation velocity (strategic perspective)

The third layer is the most important from Sean's insight—it upgrades T2V from a "pricing strategy" to an "AI-era data strategy framework."

In the AI era, the truly scarce resource isn't compute, isn't models—it's high-quality, high-frequency validation data.

This also explains why Deepractice's pivot from AI programming was correct—not because programming isn't important, but because that direction's data flywheel spins too slowly, unsuitable for startups to build moats.

Two Industry Assumptions This Concept Could Change

If the T2V Velocity concept spreads, it could change two fundamental assumptions in the AI industry:

Technology route selection: Not "which model has cheaper tokens," but "which can help customers close loops faster"

Product design logic: Not "provide more powerful capabilities," but "shorten the path from input to value verification"

This is the direction AI commercialization should really be competing in.