When you must be on the cutting edge of the intersection of finance and technology at all times, it pays to be an adamant reader.
Books are obviously a huge source of information for the world’s best investors – and we’ve looked at their book recommendations in the past – but perhaps even more telling is what they read on a day-to-day basis.
Today, we get a snapshot of the morning reading of top notch venture capitalists to see how they get the perspectives, inspiration, and insights that help drive their investments.
Morning Reading for Tech Investors
Nearly every guest on the podcast is asked to provide a blog recommendation, and Hovde has visualized this information.
The most cited blogs include AVC, Term Sheet, Mattermark Daily, and the Ben Evans Newsletter:
Despite the wild amount of variance in recommendations, here are the top seven with brief summaries and links:
- Term Sheet
- Mattermark Daily
- Ben Evans Newsletter
- Feld Thoughts
- The Information
- Strictly VC
Internet commentary from Fred Wilson, a prominent NYC-based venture capitalist. (Free)
This widely-read newsletter at Fortune was authored by Dan Primack until a month ago. However, Primack left to start a new venture. Now the column has been taken over by Erin Griffith. (Free)
A human-curated newsletter that brings perspectives, insights, and lessons learned from investors and operators in the startup ecosystem. (Free)
Benedict Evans is a partner at Andreessen Horowitz, and his newsletter highlights interesting links in technology each week. (Free)
Brad Feld has been an early-stage investor since 1987, and is best known for co-founding startup accelerator Techstars. (Free)
Not the album by Beck. Instead, it’s a popular subscription newsletter headed by Jessica Lessin that focuses on deeply-reported articles about the technology industry that can’t be found elsewhere. ($39/month)
A daily email by Connie Loizos that provides readers information related to venture capital firms, finance, and business investment. Loizos is also the Silicon Valley editor for Techcrunch. (Free)
Here’s the data again, sorted by author:
Some additional names that stand out here include Tim Ferriss, James Altucher, Bill Gurley, Tim Urban (of Wait But Why fame), and Paul Graham.
Feel free to recommend other essential reading for aspiring tech investors below.
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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