Editor’s Note: One of the biggest misconceptions about venture investing is that it’s all instinct, that the best VCs simply have a knack for spotting the next Google or Nvidia before anyone else.

My colleague Luke Lango doesn’t see it that way.

Over years of studying some of history’s greatest venture capital successes, he’s developed a straightforward framework for evaluating young companies before they ever reach Wall Street. In today’s Smart Money, he shares that process and explains why he believes it’s more important than ever now that private investing is becoming accessible to individual investors.

Then, if you’d like to see Luke apply that framework to a real AI opportunity, you can watch his free presentation, The 2026 AI Megadeal Event, immediately after today’s article.

Take it away, Luke…

In mid-20^th century , hundreds of business plans died on a single desk in Boston.

That desk belonged to Georges Doriot, a Harvard Business School professor who helped pioneer modern venture capital. Long before Silicon Valley became synonymous with startups, entrepreneurs traveled to Doriot hoping he’d write one of the rare checks that could turn an idea into a company.

His students remembered one rule above all the others because he repeated it constantly:

Consider backing a Grade A founder with a Grade B idea. Never back a Grade B founder with a Grade A idea.

He put that philosophy to the test in 1957.

A young MIT engineer named Ken Olsen came looking for funding to build a new kind of computer company. At the time, “computer” wasn’t exactly a selling point. Several high-profile failures had made investors wary, so Doriot encouraged Olsen to focus less on the technology and more on the problems it could solve for customers.

More importantly, Doriot believed in Olsen himself.

Doriot’s firm, American Research and Development, invested $70,000 in the fledgling company.

That company became Digital Equipment Corp., one of the pioneers of the minicomputer industry and, for a time, one of America’s largest technology companies. By the time Doriot’s firm exited the investment years later, its original stake had grown to more than $350 million, making it one of the landmark success stories in venture-capital history.

But here’s what most people remember about Doriot for the wrong reason.

It wasn’t that he found one extraordinary company.

It was that he had a disciplined way of separating extraordinary companies from the thousands that weren’t.

Today, individual investors have an opportunity Doriot’s generation never imagined.

For decades, if you wanted to invest in promising startups before they went public, you needed to be a venture capitalist, independently wealthy, or someone with the right connections. Today, thanks to changes in securities laws, ordinary investors can participate in many of the same kinds of early-stage opportunities that were once reserved for institutions and rich insiders.

But getting through the door is only the beginning.

Private investing is different from buying public stocks. Companies are younger, information is scarcer, and the risks are higher.

That’s why I use the same framework before I recommend any private company.

It’s a simple formula I’ve developed over years of studying venture investing. It doesn’t eliminate risk, but it helps me separate exciting stories from businesses that have the ingredients to become tomorrow’s biggest winners.

In this piece, I’ll show you exactly how that framework works.

And why I believe it gives investors a much better chance of finding exceptional companies before Wall Street ever gets a chance to buy them.

The Three Questions I Ask Before Every Investment

Every experienced venture capitalist eventually develops a checklist. Mine comes down to three questions.

I call it PPT: People. Product. Timing.

Together, they help me identify exceptional founders, products customers genuinely need, and opportunities where timing gives early investors an edge.

That’s the framework I’ll walk you through.

Will they guarantee success? Of course not.

In fact, most early-stage companies never become great businesses. Many fail outright. Even Silicon Valley’s best VC firms expect only a handful of investments to generate the majority of their long-term returns.

That’s precisely why having a disciplined process matters.

Anyone can get excited about a compelling pitch. My goal is to stay excited only about the founders and companies that still look compelling after I’ve asked the hard questions.

Let’s start with the first and, in my opinion, most important question.

People: The Filter That Matters Most

If I had to rank the three parts of my PPT framework, it would probably be People, People, People.

Or, to borrow Georges Doriot’s famous advice:

Consider backing a Grade A founder with a Grade B idea. Never back a Grade B founder with a Grade A idea.

I’ve found that rule holds up remarkably well.

A great product launched at exactly the right moment can still fail if the people running the company can’t execute, adapt when their first plan falls apart, or keep a team together through the inevitable rough patches every startup faces.

History is full of examples. The tricky part is that exceptional founders rarely look exceptional on paper.

Shopify Inc. (SHOP) co-founder Tobi Lütke left school at 16 to pursue programming after teaching himself to code as a child. On paper, that looks unconventional. In reality, he’d already spent years obsessively building software.

Lütke originally wanted to sell snowboards online. Frustrated by the e-commerce software available at the time, he built his own system instead. That side project eventually became Shopify, one of the world’s largest e-commerce platforms, powering millions of online storefronts for businesses of every size.

That’s the lesson.

The signal you’re really looking for isn’t a prestigious résumé or an impressive title. It’s deep expertise and an almost obsessive commitment to solving a specific problem. Those qualities often matter far more than where someone went to school or how polished their LinkedIn profile looks.

That’s why, before I spend much time analyzing a company’s financial projections, I spend time evaluating its founders. What have they built before? Can their claims be verified? What do former colleagues, investors, and partners say about working with them?

Great founders don’t guarantee great investments.

But I’m not trying to identify founders who are merely capable. I’m looking for the rare people who can build a company that changes an industry.

It’s an incredibly high bar, because those are the kinds of companies capable of delivering venture-capital returns.

Product: Does It Solve a Problem People Can’t Live Without?

Once I’m convinced the founders are exceptional, I turn to the Product.

The question isn’t whether it’s clever or uses the latest technology.

It’s much simpler: Does it solve a problem so important that customers will put up with an imperfect early version just to get access to it?

History suggests that’s often how category-defining companies begin.

Google is a classic example. It solved an urgent problem: finding useful information on an exploding internet. Early investors recognized that demand before most of the world did.

That’s the kind of demand I’m looking for.

Almost everything that comes across my desk is “interesting.” But I’m looking for products that people immediately recognize they need.

Today, one area where I believe we’re seeing that kind of demand emerge is physical AI. That’s the software and data that allow robots to perform useful work in the real world.

Building capable robots turns out to be much harder than building chatbots. Unlike language models, robots can’t simply learn from the internet. They have to be taught physical tasks through real-world demonstrations.

That creates a bottleneck.

Companies that can make that process faster, cheaper, or easier are solving one of the biggest obstacles standing in the way of an entire industry.

Those are exactly the kinds of AI businesses I want to investigate as private investment opportunities. They’re solving a problem that I believe customers will become increasingly desperate to solve.

Timing: Two Clocks, Not One

The last piece of my PPT framework is Timing. I watch two clocks.

First, I determine whether we’re investing early enough that there’s still tremendous upside – but late enough that the business has proven it can actually execute. That’s why People and Product come first. They help me decide whether a young company has earned the right to become an investment.

I also want the industry itself at the right moment. Today, AI is creating that kind of environment. The world’s largest technology companies are spending hundreds of billions of dollars building AI infrastructure, and increasingly buying smaller companies rather than building every technology themselves.

For early investors, that’s important.

A successful investment doesn’t always end with an IPO anymore. Often, it ends when a much larger company decides it’s faster to buy innovation than build it.

That’s why I keep an eye on both clocks.

I want to invest in the right company. But I also want to invest when the world is ready for what that company has built.

What This Framework Actually Gives You

Georges Doriot knew he couldn’t predict the future.

He was looking for conviction, the confidence that came from asking the right questions before writing the check.

That’s what my PPT framework gives me today.

Private investing will never be about finding certainty. It’s about making better decisions, more consistently.

Every opportunity I evaluate has to answer the same three questions.

Are the People exceptional? Does the Product solve a real problem? And is the Timing right?

If the answer isn’t yes across the board, I keep looking.

That’s exactly what I’ll show you in The 2026 AI Megadeal Event. Rather than talk about the framework in the abstract, I’ll apply it to a real private AI company and show you why I believe it passes all three tests.

I hope you’ll join me.

Regards,

Luke Lango

Editor, Hypergrowth Investing

P.S. Today’s article explains the framework Luke uses to evaluate private companies. But seeing that framework applied to a real opportunity is even more valuable. In The 2026 AI Megadeal Event, he’ll walk you through an AI company that he believes checks all three boxes – People, Product, and Timing – and explains exactly why it caught his attention. If you haven’t watched the presentation yet, I encourage you to do so today.

The post Most Startup Pitches Sound Great, so Here’s How to Find the Real Opportunities appeared first on InvestorPlace.

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