Shedrack Erugo

Brain–body-environment interactions must move beyond isolated exposures toward the expotype: the dynamic configuration of physical, social, lifestyle, and internal factors that jointly shape brain–behaviour phenotypes. At @NatRevNeurosci (nature.com/articles/s4158…), we propose a future agenda to assess the exposome as a complex, time-varying system that requires nonlinear models to capture interactions, thresholds, synergistic effects, and cross-domain buffering mechanisms. Although predictive machine learning can support individual risk estimation, the next frontier is to move from prediction to mechanism, from association to dynamic synergetic inference. Multivariate learning, causal machine learning, aging clocks, longitudinal designs, and generative biophysical digital twins are beginning to provide this bridge. Brain–body–environment diversity can reveal how ecology becomes biologically embedded. We call for a future exposomic neuroscience that integrates nonlinear temporal modeling, generative mechanisms, and population diversity to understand, simulate, and modify trajectories of brain aging and disease. Congrats Sarah Genon, @MasoudTahmasian & @INM7_ISN

@PhilipJohnston @AnthropicAI How do you have time to tweet when building out data centers in space?

I have interviewed 1,000s of the world's best founders over the past decade. Few have impressed me like @ShivdevRao at @AbridgeHQ. He navigated a brutal 5-year wilderness before exploding into one of the most dominant forces in vertical AI. Today, Abridge is a $5.3BN powerhouse. I sat down with Shiv to unpack exactly how he did it and condensed my notes below: 🚀 6 Lessons on Building a $5.3B Vertical AI Juggernaut 1. Survive Long Enough for Market Timing to Catch Up: Abridge spent 5 years in the "wilderness" before hitting a tidal wave of adoption. When you have an absolute true north thesis, your primary job in the early days is simple: stay standing and don’t die. You must be alive when the sky finally opens up. 2. Pivot the Product, Never the Core Thesis: Shiv was willing to pivot on features, go-to-market strategies, and business models. But he refused to budge on his core thesis that healthcare is ultimately powered by the spoken human signal. Die on the hill of your thesis; adapt everything else. 3. Target the Concentration of Scale Early: A massive trap for healthcare and enterprise founders is staying down-market too long for "fast feedback loops". In the US, the vast majority of clinicians are concentrated within large, integrated delivery networks. Time your "YOLO shot" to go up-market the moment the market inflects. Single biggest advice to founders on when to go up market @bhalligan @dharmesh? 4. Own Your Stack to Protect Your P&L and UX: While many AI startups rely entirely on frontier systems, 40% of Abridge's model outputs are generated by in-house models. Milliseconds matter in high-stakes enterprise workflows. Building your own models gives you insane performance gains, lower latency, and ultimate control over your P&L. When should you vs should you not build your own model @matanSF @MaxJunestrand @antonosika? 5. Don't Fight Foundation Models—Counter-Position Instead If you try to fight the frontier model giants directly, you've already lost. You win by going millions of miles deep into regulated industries with proprietary datasets and workflows they can't easily replicate. Find ways to coexist and leverage their tailwinds. Reminds me of what @bradlightcap said on his 20VC. 6. Move Toward the "Flat Company" Era: With the explosion of AI agents and advanced tooling, the traditional management layer is compressing. Shiv’s latest idealistic shift is building a hyper-flat organization: fewer managers, and highly leverageable "Super ICs" who can move in lockstep and cover massive surface area. (link in comments)