Vision Without Validation Is Like Gambling

Check Demand Before You Invest

Most startups don’t fail because the tech doesn’t work. They fail because market demand doesn’t meet expectations. About 75% of venture-backed companies never return capital. And in the AI boom, the odds are even worse: 95% of generative AI projects fail to deliver meaningful results, despite billions in funding.

The common thread? Weak product–market fit (PMF). According to CB Insights, over half of critical failures were tied directly to poor product–market fit. Ultimately, marketing missteps outweighed every other cause combined (56% vs. 44%)

Here’s the uncomfortable truth: companies can build flawless products, but without validated demand, even the most well-funded products won’t deliver adequate returns on investment.

History is full of examples — from Google Glass and the Segway to more recent flameouts like FTX, Builder.ai, and the Humane AI Pin, where world-class engineering and marquee investors still couldn’t create demand.

When Market Hype Masks Demand Reality

Even elite firms with unparalleled resources can be blindsided when the underlying demand isn’t there:

• Sequoia Capital – FTX: Wrote off its entire $214M investment (The Information, Private Equity Wire, Unchained).

• SoftBank Vision Fund – Builder.ai: Losses contributed to a record $27.4B annual hit.

• Tiger Global / Microsoft / OpenAI – Humane AI Pin: Raised hundreds of millions, now widely considered a flop (Financial Times, Business Insider).

👉 Lesson for investors: Deep funding ≠ market demand.

“Vision without validation is gambling.”

The Limits of Market Reports — They Don’t Reveal Demand

Most VCs rely on market reports to assess product viability. These reports size the total addressable market, project growth rates, and benchmark competitors , which are all useful for understanding sector potential. But they don’t answer the most important question: Will anyone actually adopt this product?

A market can look massive on paper yet still fail if consumers don’t perceive the product as useful, easy to use, enjoyable, identity-aligned, or worth the price. This blind spot is why weak product–market fit remains the leading cause of startup failure, and where a complimentary approach is needed.

From Research to Investment Tool

During my PhD, I set out to go beyond guesswork and hype. I developed what became the Go/No-Go Framework, combining behavioral science with machine learning to both quantify demand likelihood and explain the reason why consumers accept or reject a product.”

The framework revealed not only whether people were likely to adopt, but which human factors: usefulness, ease of use, identity fit, enjoyment, and perceived value were driving or blocking demand. That insight became the foundation for Go/No-Go: an evidence-based system VCs can apply before writing the check.

Why Perception, Not Specs, Drives Adoption

A product’s technical specs don’t create market demand. What matters is how people perceive it.

• Usefulness (PU): Will this improve my life or performance?

• Ease of use (PEOU): Does it feel intuitive and simple?

• Enjoyment (HM): Is it fun and satisfying to use?

• Identity fit (SOI, SEI): Does it align with who I am and my social circle?

• Value (PV): Do the benefits justify the cost?

• Trust: Do I believe this product is safe and reliable?

Market reports rarely capture these perceptions. Yet they are the levers that determine whether consumers buy, stay, and recommend — the outcomes that decide whether a VC bet compounds or collapses.Write your text here...

When Behavioral Science Meets Predictive Analytics

When you combine behavioral data with predictive modeling, investors can:

• Collect insights from real target users, not generic panels.

• Use machine learning to quantify demand likelihood before deploying capital.

• Explain predictions with behavioral science, showing why adoption will or won’t happen.

• Run simulations to make confident Go / Revise / No-Go calls.

• Align fund ROI objectives with demand probability, not hype cycles.

This is the essence of the Go/No-Go Framework: turning vague “market interest” into a quantified probability of success, before VC capital is at risk.

A Real World Test: AR Virtual Try-On

In my PhD research on augmented reality virtual try-on (AR VTO), survey data from 424 consumers in Vietnam showed what really drives adoption (measured on a 1–5 Likert scale):

· Hedonic motivation (4.5/5) was the strongest driver.

· Perceived usefulness (3.7/5) second strongest driver.

· Perceived value (3.6/5) third strongest driver.

· Ease of use (3.2/5) mattered but wasn’t decisive.

· Identity alignment (3.4/5) varied significantly by age and gender.

Even small tweaks in messaging, emphasizing playfulness for younger consumers vs. clarity and ease for older ones measurably increased intent to adopt without changing the product itself.

Why This Matters for Investors

With behavioral science + predictive analytics, VCs can:

🚫 Avoid portfolio drag: Spot low-demand products before they consume capital.
⚡ Accelerate returns: Back the companies most likely to reach traction.
💰 Use capital efficiently: Fund validated demand, not hype.

By measuring what people will do, not just what they say, you can confidently allocate capital toward the winners and confidently cut losses early.

Closing

Vision without validation is gambling.

That’s why I built Go/No-Go: to give investors a systematic way to test demand before committing capital.

👉 If you’d like to explore how this could fit into your diligence process, connect with me on LinkedIn or visit go-no-go-insights.com.

Caption: Behavioral drivers of AR VTO demand among 424 consumers in Vietnam (1–5 scale).

Caption: The Go/No-Go Framework evaluates demand probability before major investment.

Caption: Market demand hinges on perceptions of usefulness, ease of use, enjoyment, identity fit, and value.

Caption: Consumers ultimately decide — not investors, not engineers.

Caption: Startup shutdowns: 56%of the failures stem from marketing mistakes — with lack of product–market fit the single biggest reason companies close.