In 2011, two startups were in a head-to-head race to reinvent urban transportation: Uber and Sidecar.
- Both had traction
- Both had hype
- Both attracted the attention of top-tier venture capital firms and world-famous investors
- Union Square Ventures
- Lightspeed Venture Partners
- Google Ventures
- Avalon Ventures
- Sir Richard Branson
They all placed their bets on Sidecar.
On paper, the company looked strong. It had a compelling narrative, an early-mover advantage, and the endorsement of respected names in the industry. Sidecar’s team was charismatic, its product innovative, and its growth promising.
Yet history played out very differently. While Uber scaled into a global transportation giant, Sidecar shut down in 2015.
The Problem with Hype-Driven Decisions
Venture capital has always thrived on instincts. The best investors are known for spotting trends before they go mainstream, for backing founders who don’t fit the mold, and for making contrarian bets that later look obvious in hindsight.
But instincts, no matter how legendary, are also human. They’re shaped by personal experience, network effects, and the momentum of the moment. And in high-stakes decisions, hype can distort reality.
That’s exactly what happened with Sidecar. It had all the right signals on the surface, but those signals weren’t standardized. They were fragmented, subjective, and incomplete.
What Rocketbeet Would Have Shown
If Rocketbeet had been there at Series A, the risk levels would have told a different story:
- Uber: 777 / 1000 (Lower Risk Signal)
- Sidecar: 437 / 1000 (Higher Risk Signal)
Our algorithm would not have guaranteed that Uber would succeed or that Sidecar would fail. Startups are inherently risky, and both could have collapsed.
But the Score would have quantified the relative risk of each investment, showing that Uber was the safer bet compared to Sidecar.
In other words, Rocketbeet provides a way to separate hype from fundamentals, making it clear which startup carries more investability risk at any given stage.
Why This Matters
This is not about saying investors were “wrong.” After all, some of the smartest VCs in the world made the same call. It’s about recognizing that in a world overflowing with data, pitch decks, and market noise, gut instinct alone isn’t enough anymore.
That’s where Rocketbeet comes in.
Our mission is not to replace investor expertise. We know venture capital will always be part art and part science. Instead, we exist to complement the art with science, to provide a risk signal that is:
- External – outside of the hype cycle, not swayed by founder charisma or market buzz.
- Neutral – free of investor bias, based on consistent data-driven variables.
- Standardized – comparable across startups, sectors, and geographies.
Think of Rocketbeet as the FICO Score for startup investability risk. Investors still bring their judgment, networks, and thematic expertise. But with Rocketbeet, they also have a market-standard external signal to validate or challenge their instincts.
Lessons from Sidecar
Sidecar’s story isn’t unique. History is filled with examples of startups that looked like winners but failed to scale, and others that were overlooked until it was almost too late.
The lesson is clear:
- Hype is not a signal.
- Charisma is not traction.
- Investor pedigree is not a predictor of success.
- Risk can be measured.
Rocketbeet doesn’t eliminate risk, but it makes risk visible, comparable, and standard.
High Signal. Less Noise.
Even the best gut instincts need data. That’s the foundation of Rocketbeet.
By turning fragmented, subjective impressions into standardized risk signals, we help investors make safer decisions, faster, and help founders who truly have what it takes to scale stand out in a crowded ecosystem.
If Rocketbeet had scored Uber vs Sidecar at Series A, the difference would have been clear:
- Both involved risk.
- One riskier than the other.
Uber carried the lower risk signal. Sidecar carried higher risk hidden under the hype.
That’s the power of the Rocketbeet Score.
Rocketbeet: High Signal. Less Noise.