Technology
How Startups Can Improve Their Odds of Becoming a Unicorn
In 2017, we showed that 57 startups were able to achieve unicorn status – a rare designation that is reserved only for privately-held startups valued at $1 billion or more.
While this number may seem high, unicorns are still quite the rarity.
In the U.S. alone, there are currently 19,550 venture-backed startups vying for those same massive valuations. At the same time, it’s been estimated that each new startup only has a 0.00006% chance of becoming a billion dollar company.
How to Improve Those Odds
No one ever said that joining the ranks of unicorns would be easy, but there is some good news for aspiring founders.
Today’s infographic, which comes to us from FounderKit, looks at traits of existing unicorns – and analyzing this wealth of data might help entrepreneurs in shaping their own companies for future success.
Put together with information from Fortune and Crunchbase, this infographic gives us some clues as to how game-changing unicorns have been built in the past.
While it’s certainly not a prescription for future success, it does provide a blueprint for what’s needed to improve your chances of beating the odds.
Playing the Red Team
If you’re an entrepreneur with billion dollar dreams, take a close look at the categories that best resemble your startup.
For example, if your model depends on leasing hardware to the energy sector as a major revenue source, you should note that the odds are mostly against you. For starters, only 7% of unicorns are hardware companies, and energy doesn’t register high as a major business sector that has seen many unicorns. Further, companies that rent or lease their physical or intellectual assets make up just 1% of recent unicorn companies, which makes this particular model look pretty disadvantageous.
It doesn’t mean that this idea is not feasible – maybe it’s an underappreciated sector, or the idea is completely groundbreaking. However, given the information above, it’s most likely that this will be a tough go, so it’s worth making adjustments accordingly.
Playing the Green Team
Based on the above information, what combination of startup traits could provide the most common recipe for unicorn status?
Let’s create a hypothetical new startup:
- It should be consumer focused, since the majority of companies are B2C (62%)
- It should provide software, since 87% of all unicorns focus there
- This startup should be retail/e-commerce marketplace focused, a category home to a whopping 25% of recent unicorns
- It should have a model based on commission or brokerage fees (33% of recent unicorns)
It’s not hard to see similarities with the above traits and recent unicorns like Shopify or Airbnb, which both serve as solid precedents for success.
Of course, it’s far from a guarantee of future unicorn status, but it does mean that you likely have better than a 0.00006% chance.
Technology
Charted: The Jobs Most Impacted by AI
We visualized the results of an analysis by the World Economic Forum, which uncovered the jobs most impacted by AI.
Charted: The Jobs Most Impacted by AI
This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
Large language models (LLMs) and other generative AI tools haven’t been around for very long, but they’re expected to have far-reaching impacts on the way people do their jobs. With this in mind, researchers have already begun studying the potential impacts of this transformative technology.
In this graphic, we’ve visualized the results of a World Economic Forum report, which estimated how different job departments will be exposed to AI disruption.
Data and Methodology
To identify the job departments most impacted by AI, researchers assessed over 19,000 occupational tasks (e.g. reading documents) to determine if they relied on language. If a task was deemed language-based, it was then determined how much human involvement was needed to complete that task.
With this analysis, researchers were then able to estimate how AI would impact different occupational groups.
Department | Large impact (%) | Small impact (%) | No impact (%) |
---|---|---|---|
IT | 73 | 26 | 1 |
Finance | 70 | 21 | 9 |
Customer Sales | 67 | 16 | 17 |
Operations | 65 | 18 | 17 |
HR | 57 | 41 | 2 |
Marketing | 56 | 41 | 3 |
Legal | 46 | 50 | 4 |
Supply Chain | 43 | 18 | 39 |
In our graphic, large impact refers to tasks that will be fully automated or significantly altered by AI technologies. Small impact refers to tasks that have a lesser potential for disruption.
Where AI will make the biggest impact
Jobs in infogramtion technology (IT) and finance have the highest share of tasks expected to be largely impacted by AI.
Within IT, tasks that are expected to be automated include software quality assurance and customer support. On the finance side, researchers believe that AI could be significantly useful for bookkeeping, accounting, and auditing.
Still interested in AI? Check out this graphic which ranked the most commonly used AI tools in 2023.
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