Technology

What’s Hot (and Not) in Early Stage Tech

Published

7 years ago

May 27, 2016

Jeff Desjardins

What's Hot (and Not) in Early Stage Tech [Chart]

What’s Hot (and Not) in Early Stage Tech [Chart]

Using big data to discover what aspiring entrepreneurs are thinking

The Chart of the Week is a weekly Visual Capitalist feature on Fridays.

There are many ways to get a pulse on the startup scene to see what is trending. For example, one could look at the sub-sectors getting the most money from venture capitalists. The more deals and money hitting a sub-sector, the more it could be on its way up the ladder.

However, perhaps there is another angle that can tell us something, even if it’s simply confirming an already-held suspicion about trends in early stage tech. What are the entrepreneurs in the trenches doing? What are they focusing on, and how is that a change from previous time periods?

Big Data from Y Combinator

Y Combinator, arguably the most prominent startup accelerator on the planet, has indulged us on this hunch. Using the thousands of applications they get each year from aspiring entrepreneurs, they’ve had the foresight to methodically break them down by keyword to potentially show us trends within the pitches by startup founders.

For a wonderful post that breaks this all down, go to the company’s The Macro blog, which discusses many of these trends over the course of years in great detail.

That said, we decided to piggyback onto this interesting data set with a slightly different approach.

Method to the Madness

While the results of the keyword analysis of Y Combinator applications included many meaningful keywords, it also was cluttered with less meaningful pieces of noise. As an example, between 2015 and 2016 applications, there was a 204% increase in the use of the word “firms”. This doesn’t seem to tell us anything significant about the startup world, especially since it only went from 0.3% to 0.8% in actual usage within the scope of all applications.

To combat noise, we took the more subjective approach by identifying keywords that were more concretely associated with sub-sectors or trends. The mention of the term “IoT” in an application, for example, is more telling and suggests that an entrepreneur is pitching a startup idea related to the Internet of Things to the accelerator. More mentions of “IoT” in pitches means that ideas on the “IoT” are top of mind for aspiring entrepreneurs.

What’s Hot in Early Stage Tech?

Using the above subjective methodology, here are the increases and decreases over the last year that stood out the most to us:

The word “Slack” was used 850% more often in 2016 applications, clearly related to the popular workplace collaboration tool of the same name. Slack’s explosive growth has rippled through to the startup world, likely inspiring an army of potential competitors and collaborators in the wake of their success.

Other emerging trends that picked up steam in recent applications: virtual reality (“VR”), artificial intelligence (“AI”), internet of things (“IoT”), and “drones”. The 119% increase in the usage of the word “bills” also points to the recent attention on the fintech space.

The mention of “SaaS” (Software as a Service) also increased 52%, as it has become a preferred business model by venture capitalists.

What’s Not

The largest decrease of all terms used was that of “Bitcoin”, which dropped 62% from one year to the next. The cryptocurrency has been a popular developer target for years, but the rush to take it mainstream may now be losing steam. The world’s top performing currency in 2015 has been called dead many times before, so it is certainly no stranger to adversity.

The word “nonprofit” was also used 29% less, which may point to the recent pressure for startups to offer a more foreseeable potential return on investment for investors. The ecosystem isn’t as frothy as it once was, and nonprofit ideas may have taken a temporary tumble as a result.

“Crowdfunding” has also dropped more off the radar, receiving 25% less mentions.

Related Topics:bitcoin startups tech slack y combinator

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Technology

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.

Published

3 days ago

February 1, 2023

Nick Routley

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

Disrupting Industries

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.