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.
Infographic: Generative AI Explained by AI
What exactly is generative AI and how does it work? This infographic, created using generative AI tools such as Midjourney and ChatGPT, explains it all.
Generative AI Explained by AI
After years of research, it appears that artificial intelligence (AI) is reaching a sort of tipping point, capturing the imaginations of everyone from students saving time on their essay writing to leaders at the world’s largest tech companies. Excitement is building around the possibilities that AI tools unlock, but what exactly these tools are capable of and how they work is still not widely understood.
We could write about this in detail, but given how advanced tools like ChatGPT have become, it only seems right to see what generative AI has to say about itself.
Everything in the infographic above – from illustrations and icons to the text descriptions—was created using generative AI tools such as Midjourney. Everything that follows in this article was generated using ChatGPT based on specific prompts.
Without further ado, generative AI as explained by generative AI.
Generative AI: An Introduction
Generative AI refers to a category of artificial intelligence (AI) algorithms that generate new outputs based on the data they have been trained on. Unlike traditional AI systems that are designed to recognize patterns and make predictions, generative AI creates new content in the form of images, text, audio, and more.
Generative AI uses a type of deep learning called generative adversarial networks (GANs) to create new content. A GAN consists of two neural networks: a generator that creates new data and a discriminator that evaluates the data. The generator and discriminator work together, with the generator improving its outputs based on the feedback it receives from the discriminator until it generates content that is indistinguishable from real data.
Generative AI has a wide range of applications, including:
- Images: Generative AI can create new images based on existing ones, such as creating a new portrait based on a person’s face or a new landscape based on existing scenery
- Text: Generative AI can be used to write news articles, poetry, and even scripts. It can also be used to translate text from one language to another
- Audio: Generative AI can generate new music tracks, sound effects, and even voice acting
People have concerns that generative AI and automation will lead to job displacement and unemployment, as machines become capable of performing tasks that were previously done by humans. They worry that the increasing use of AI will lead to a shrinking job market, particularly in industries such as manufacturing, customer service, and data entry.
Generative AI has the potential to disrupt several industries, including:
- Advertising: Generative AI can create new advertisements based on existing ones, making it easier for companies to reach new audiences
- Art and Design: Generative AI can help artists and designers create new works by generating new ideas and concepts
- Entertainment: Generative AI can create new video games, movies, and TV shows, making it easier for content creators to reach new audiences
Overall, while there are valid concerns about the impact of AI on the job market, there are also many potential benefits that could positively impact workers and the economy.
In the short term, generative AI tools can have positive impacts on the job market as well. For example, AI can automate repetitive and time-consuming tasks, and help humans make faster and more informed decisions by processing and analyzing large amounts of data. AI tools can free up time for humans to focus on more creative and value-adding work.
How This Article Was Created
This article was created using a language model AI trained by OpenAI. The AI was trained on a large dataset of text and was able to generate a new article based on the prompt given. In simple terms, the AI was fed information about what to write about and then generated the article based on that information.
In conclusion, generative AI is a powerful tool that has the potential to revolutionize several industries. With its ability to create new content based on existing data, generative AI has the potential to change the way we create and consume content in the future.
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