I’ve seen a lot of frustration towards Mistral AI and their $113M seed round on social media lately. Here are some of my reflections about it. But before we start I have to, of course, point out that we wouldn’t be here without Transformer models (first described in the Google Brain paper in 2017), which changed the game for language models. — Let’s deep-dive right away into it. I’ve decided to write this article mainly in a bullet-point format to make it snappier. I also recommend looking through the 7-page Mistral AI investment memo published by Sifted below:

Since the end of November and the launch of ChatGPT, the discussion around AI/ML has been entirely dominated by generative AI, Large Language Models, and Foundation Models. This is for a good reason some may say, as the masses have realized the power of these models, especially when they’re fine-tuned with reinforcement learning from human feedback. But it’s equally important to understand that AI/ML is much more than just LLMs & Foundation Models. I briefly wrote about this in my post about the hype around LLMs and Foundation Models earlier in 2023. This was one of the reasons why it felt like a breath of fresh air when Twitter decided to open source its recommendation algorithm — something that Elon had been teasing about for a while.

Wiz — a multi-cloud security startup founded in 2020 — just raised 300M$ at a $10B valuation. In this market climate. How is this possible? 🤯 — Well, Wiz is unique in many ways, but most importantly the startup is a masterclass of top-percentile business execution, founder-market fit and playing on strong secular tailwinds with the right GTM. Wiz is the fastest SaaS company to reach $100M in ARR and $200M in ARR respectively and has changed…

There are 36 AI/ML-related startups in this YC Winter Batch out of the 201 startups accepted in total. — This represents ca 18% of the current cohort being some kind of Generative AI startups. Is 18% a lot or not? Let’s compare this with the Y Combinator Winter & Summer Batch from ’21 when we were at peak Crypto & Web3 hype: The ’21 YC Winter Batch had in…

The latest MAD (Machine Learning, AI and Data) Landscape is out again! A huge kudos for the tremendous work Matt Turck and the team FirstMark Capital have done putting the analysis together 🙌 — In this year’s landscape, there are 1,426 companies, compared to 139 companies in the first landscape from 2012. This represents an +1,000% increase in a decade!

In the US alone 340 unicorns were minted in 2021 according to data from PitchBook. This compared to 100 new US unicorns minted in 2020, 78 unicorns in 2019, 62 unicorns in 2018, 35 unicorns in 2017 and 21 unicorns in 2016. In 2021 it felt like new +$1B companies were minted every hour. We’ve not only experience overall inflation, but unicorn inflation like never seen before — not only in the US but globally.

We’ve all seen the craze around generative AI via e.g. ChatGPT, DALL-E, and Stable Diffusion. GPT-4 is rumored to be even more mind-blowing. There are plenty of open questions about these models, their accuracy, and even their legality from an IP perspective. And from the startup and investor perspective, there are serious concerns about whether one can build a defensible business on top of any of these types of foundation model platforms. But hype cycles come and go. Many are not aware of this, but we’ve had several AI hype cycles followed by several AI winters. Did you know that the first chatbot launched was Eliza in 1964? It was an early natural language processing computer program created from 1964 to 1966 at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.

This was a blog post that I already planned to write during the spring when I saw that the hype around DuckDB started taking new heights. Since then the discussion around DuckDB has only intensified in the developer and data engineering community. I currently see two trends within the data community with high engagement levels: DuckDB and Rust taking over data engineering. But what’s the hype around DuckDB really about? Let’s scratch the surface a little bit.

In a few weeks, it will be exactly ten years since AWS Redshift was made public for the first time through a limited preview. Redshift is considered the O.G. cloud data warehouse, followed by BigQuery and Snowflake. …