lover, not-fighter. building and funding startups: @kindredventures, @heartbeat
on my 📲 too much
Joined October 2008
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Kanyi Maqubela retweeted
Big congratulations to the @fal team on their $1.65B financing led by @sequoia, @BessemerVP, and @nvidia and their phenomenal growth as the foundational inference and post-training platform for the generative media space! Led by @burkaygur and @gorkem, the company hosts and optimizes over 3000 models now, and provides the fastest inference and custom model development for both AI-native builders and Fortune 500 companies.
More on our continued investment and support of their awesome journey:
kindredventures.com/announce…
let’s goooooooo. this industry was still in the village before the base before we met fal. it was at the foot of the mountain when we led the A… a whole mountain ahead, even now.
let’s go @stevejang for leading us on this and @burkaygur @gorkem @isidentical and crew for being our sherpa. 🫶🏾🫶🏾🫶🏾
when I first met @burkaygur @gorkem and @isidentical 3 years ago, we bonded on inference, diffusion models, and not-yet-arrived gen video models.
starting with just a handful of team members and early customers, they have now accelerated into becoming the trusted foundational infrastructure for the exploding gen media space.
with a concerted move into more compute (1gw 🔜), more model post-training 🪄, and agent tools 🤖, the @fal team is pushing the frontier on many levels for the gen media wave.
congrats to the entire team from all of us at @KindredVentures! we're so inspired by your journey, excited to quintuple down with you in this round, and excited for what's next 🔥🔥🔥
Absolutely legendary. Still in the beginning of the journey. And they are hiring!
cant say this enough: still early days.
a BIG thank you for the team who work their days and nights to get us here.
come be part of a generational team:
fal.ai/careers
Kanyi Maqubela retweeted
Today, we’re announcing we raised our Series E & F rounds for a combined total of $1.65B. Our Series E was led by @sequoia and our Series F was led by @BessemerVP with continued investment from @nvidia.
We’re excited to push the boundaries of what’s possible even more next year and this funding will enable the team to build the way we want to.
besides being hilarious, this belongs in a time capsule. it will be ancient history very soon, and it’s impermanence makes it beautiful.
“you had to be there”
Hearing from MBA students re: which industries are interested in them this recruiting season:
- tech is way down
- consulting is way down
- banking is way *up*
Kanyi Maqubela retweeted
We spent $600B - $1.2T in real dollars fixing Y2K. It was a huge effort!
The lack of major problems from Y2K was more a result of solid preparation than misplaced fears.
Shout out to @janedunlevie, who has been on a generational heater the last 5 years.
Kanyi Maqubela retweeted
The new Holon-S1 model from @SophontAI is a strong example of how specialized domain-specific models can compete with or beat pure-scaling approaches in many fields - in this case, medical AI. Kudos to @iScienceLuvr @humanscotti and the Sophont team! Proud of you at @KindredVentures 👏👏👏
Cancer patients with the same diagnosis often have very different tumor biology.
Molecular testing can reveal underlying differences, but that's slow and expensive.
Introducing Holon-S1, our platform to predict genes and biomarkers from routine pathology slides.
• beats two other models at predicting spatial gene activity in a controlled comparison
• has identified potentially novel biomarkers in breast cancer and colon cancer
• evaluated across 16 organ types and more than 15 scanners, using slides from both formalin-fixed, paraffin-embedded and fresh-frozen tissue.
We believe Holon-S1 has the potential to help researchers find new biomarkers in slides they already have, discover treatment targets, and bring more personalized cancer care within reach!
Kanyi Maqubela retweeted
AI x bio has had an insane week.
> Radical Numerics: Omnii-1 designed phages given to a human patient
> Claude: 17 scorpion-toxin binders validated in the lab
> OpenAI: acquired Orchestra Bio
> OpenAI + PerturbAI: agents found new neurodegeneration leads in 32 minutes
> RFD4-Proteina: open programmable protein design model
> BioEmu2: new biomolecular simulation model
> Chaperone: agent framework connecting 50+ protein design tools
> Xaira: launched X-Scientist for agentic drug discovery
> Sophont: Holon-S1 predicts gene activity from pathology slides
> Boltz + BMS: bringing biomolecular models into pharma workflows
> Phylo + Bayer: deploying Biomni Lab across research teams
> Kevin Weil, ex-OpenAI, launches Warp Sciences: AI + robotic labs for drug discovery
> Carbon-X: open DNA model its team says matches GPT-6.1 Sol at ~1,000x lower cost
This is happening across the whole stack: models, protein design, simulation, agents, robotic labs, pathology, pharma workflows, and actual patient use. Bio is becoming computer.
Seems like a good week to review the annual revenues for some of the biggest individual drugs (brand, not molecule). 2025 figures:
Keytruda: $31.7b
Mounjaro: $22.9b
Ozempic: $19.2b
Dupixent: $17.7b
Skyrizi: $17.5b
Kanyi Maqubela retweeted
Sophont CEO @iScienceLuvr explains how Holon-S1 can extract insights that usually require expensive molecular tests from an ordinary cancer biopsy slide:
"Why is it so hard to cure cancer? Because cancer’s a very diverse disease. Even though two people may have the same exact diagnosis, the underlying biology can be quite different."
"There are various tools for doing this, molecular tests and so on, but a lot of these tools can be quite expensive and time-consuming. Can we extract a lot of those similar insights directly from a tissue slide?"
"Whenever a cancer patient has a solid tumor, the gold standard is you take a biopsy, put it on a slide, under the microscope, and a doctor is analyzing that."
"We can take that same exact slide and with AI extract a lot more information about what’s happening at the molecular level, what genes are being expressed, and what makes that tumor unique for that patient."
@SophontAI
Cancer patients with the same diagnosis often have very different tumor biology.
Molecular testing can reveal underlying differences, but that's slow and expensive.
Introducing Holon-S1, our platform to predict genes and biomarkers from routine pathology slides.
• beats two other models at predicting spatial gene activity in a controlled comparison
• has identified potentially novel biomarkers in breast cancer and colon cancer
• evaluated across 16 organ types and more than 15 scanners, using slides from both formalin-fixed, paraffin-embedded and fresh-frozen tissue.
We believe Holon-S1 has the potential to help researchers find new biomarkers in slides they already have, discover treatment targets, and bring more personalized cancer care within reach!