View from Seed
SEPTEMBER 8, 2022
So we are now at the dawn of Generative AI. In the realm of visual imagery, Generative AI has been used to create realistic images of nearly anything conceivable, from people who don’t exist (yet) to entirely new animals. We are only at the beginning of understanding how Generative AI will change the world as we know it.
VC Cafe
FEBRUARY 27, 2023
Taking stock of the venture capital market in 2023, it’s clear to see that we’re in a transition point. In today’s market, I believe small is beautiful, and that specialisation matters. Seed and pre-Seed investment levels and valuations remain healthy, there’s more talent in the market and less competition.
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Hunter Walker
MAY 17, 2024
That premise turned out to be true, as we’re all seeing play out today with the rise of AI APIs that many applications are rushing to adopt. Textio brought this vision to market with our first product, designed to help people write job posts that attracted diverse and qualified candidates. The first one is simply focus.
VC Cafe
OCTOBER 4, 2022
Generative AI is well on the way to becoming not just faster and cheaper, but better in some cases than what humans create by hand. Creative AI or Creative automation is increasing at a rapid pace. The application landscape, while still in the early stages, seems to be rapidly growing. How far can this tech go?
VC Cafe
JUNE 6, 2024
It’s not a surprise, given that entrepreneurs are obsessed with data and metrics, but in the conservative VC market of 2024, it feels even more important for founders to know what ‘good’ looks like and what investors expect. ARR growth rates decreased significantly in 2023 and growth stage companies were most impacted.
VC Cafe
FEBRUARY 27, 2024
Seed is about showing initial product market fit. Below are just a few contributors to the rise of pre-seed in the current market: Explosion of Micro funds – In recent years, there’s been a steep increase in the number of micro funds, which are generally below $100M in size.
VC Cafe
JULY 7, 2023
Most of the companies that raised these mega rounds are relatively early stage in terms of product and monetisation (if at all) and about half of the companies that raised large sums, are developing LLMs, which generally requires deep pockets to cover 1) cloud costs, 2) expensive deep learning engineers 3) get the data to train the models.
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