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…

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. …

We are excited to announce that Validio has joined the Google Cloud Partner Advantage program as a Build Partner! — Validio’s next generation data quality validation and monitoring platform will enable joint customers of Validio and Google Cloud to achieve more trust in their data and data pipelines, proactively monitor data health, and identify and remediate data quality issues as they arise. Validio supports both batch data and streaming data…

In 2021, we saw quite an acceleration of the buzz around the rise of the Modern Data Stack. We now have a tsunami of newsletters, influencers, investors, dedicated websites, conferences, and events evangelizing it. The concept around the Modern Data Stack (albeit still in its early innings) is tightly connected with the explosion of data tools in the cloud. The cloud comes with a new model of infrastructure that will help us build these data stacks fast, programmatically, and on-demand, using cloud-native technologies like Kubernetes, infrastructure as code like Terraform and cloud best practices of DevOps. So, infrastructure becomes a critical factor in building and implementing a Modern Data Stack.

I still remember how not that many years ago it was common for people to refer to a data scientist as a “unicorn”. The expectation level was set at a super full-stack engineer/mathematician who could understand all business problems. However, during the past 2 years, as we’ve passed the peak AI/ML hype, we’ve witnessed the rapid rise of the data engineer. Dice’s 2020 tech jobs report cites data engineering as the fastest-growing job in tech in 2020, increasing by a staggering 50%.