Data layer for health. We’re building the agentic context layer for techbio + deep tech. We’re starting with the AI data scientist for precision medicine.
We’re joining @govclab ‘s accelerator! We will leverage fully automated fund formation, agentic AI infrastructure, and systematic capital flow in order to underwrite the tech stack of the next century!
@agupta@JTLonsdale Wrong @agupta . Academia has proven to a terrible partner especially if you add national security considerations. Invest via agencies like ARPA-H and DARPA directly in innovative USA industry projects and companies. @JTLonsdale & co. have discussed both points.
Founder @avelezarce will be in ATX next week for @CapitalFactory 's @healthsupernova. Their premiere healthcare event!
@DrLuisEMartinez and Drew Yashar manage the practice and we're excited to connect.
All - please reach out if you'd like to discuss the furture of AI for science and engineering.
An official statement as to what Year Zero means and what we stand for in our work against the delayed future and catalyzing breakthroughs at the input layer of technology:
https://t.co/5ZWI2go2Au
Biomedicine Just Became Executable. ArcellAI Built the Runtime.
We are building the Operating System Powering the Next Generation of Precision Medicine
https://t.co/o1i1rAlge4
@clickup has outsourced their entire customer support and they’ve become a completely useless function. Do not use them if agentic capabilities are the selling point for you.
BioAI is failing because biology still lacks a computable state layer. We just published a new essay on a belief that sits at the center of ArcellAI:
We learned how to program computers. We never learned how to program biology.
We have models. We have data. We have tools.
What we still do not have is a system that makes biological and clinical reality explicit, structured, and computable. That missing layer is why AI still breaks across real scientific and clinical workflows.
This piece is about that gap, and about the future unlocked once biology has the equivalent of a true state layer:
- research systems that continuously integrate new data
- clinical systems that adapt to evolving patient state
- biological engineering that becomes increasingly programmable
If we get this right, biological engineering starts to move less like the world of atoms and more like the world of bits. The rate of innovation in biology and medicine begins to resemble software.
That is the direction we’re building toward at ArcellAI.
#AI #TechBio #Biotech #ClinicalResearch #ClinicalAI
Technical education is undergoing structural change.
Universities, online programs, and corporate training each solve part of the problem, but none fully align incentives with real-world impact.
In this article, we discuss why company-built education may be the next model.
@DanielDiMartino When a system is funded independently of the value it produces, prices can rise without improvement. Hi from @avelezarce@DanielDiMartino https://t.co/96E9LLi1hY
@DanielDiMartino When a system is funded independently of the value it produces, prices can rise without improvement. Hi from @avelezarce@DanielDiMartino https://t.co/96E9LLi1hY
hey @AstasiaMyers@avelezarce here.
your view on devtools starting with extreme single-player value + scaling to org-wide systems feels spot on, especially in complex AI infra.
we’re building the agent-native context + execution layer for deploying AI in fragmented, multimodal systems (techbio, healthcare, engineering). not a copilot, the underlying infra that makes these systems actually work in production.
we have early design partners with path to $200k+ ARR.
curious if this resonates, worth a quick chat?
deck: https://t.co/1LOIIJGOyC
memo: https://t.co/vlKQu0xW0D
[email protected]