Latent Space

RJ Honicky × Matt McPartlon × Neil Patil

Chai Discovery is commercializing a neutral modeling and product layer for pharma rather than developing its own drugs, with partnerships including Eli Lilly, Pfizer, Novartis, and Genentech.Chai-2 designed antibodies against 50 targets, finding binders for about half with an average binding hit rate of around 20%, while enabling GPCR agonists and multispecific formats traditional screening struggles to produce.The opportunity depends on improving developability and epitope prediction, but compute scarcity, validation latency, and talent shortages remain structural constraints as Chai scales its platform.

swyx × Vibhu × Philip Kiely × Ali Taha

Inference remains an early optimization market: on identical hardware, quantization, caching, speculation and traffic tuning can typically deliver 2–4X gains, while production support requires weeks of debugging.The economics move customers from pay-per-token trials to dedicated capacity, as reliability, isolation and workload-specific tuning justify self-saturating infrastructure; faster interconnects and training-integrated optimization remain the next catalysts.

swyx × Vibhu × Akshay Nathan

ChatGPT Work has reached 10 million users by extending Codex’s agentic capabilities beyond developers, but it remains paid-only and not ChatGPT’s default.OpenAI is standardizing one harness across Codex and Work, while Sites, artifacts, and persistent context move the product above traditional applications.The next catalyst is broader distribution into knowledge work and personal workflows; permissions, trust, and misleading productivity metrics remain unresolved risks.

swyx × Vibhu × Eiso Kant

Poolside’s claimed moat is a model factory that turns checkpoints into repeatable launches, running 10,000–20,000 experiments monthly with fewer than 70 researchers and roughly 35 engineers.Laguna S suggests persistence and verification can offset parameter scale—118B total, 8B active—while open research could widen competition, though Poolside still lacks a complete business model and must scale with frontier rivals.

Bo Wang × Ci Chu

Xaira links protein design, X-Cell, and patient-representation models around a causal drug-discovery loop, arguing that observational expression profiles cannot answer intervention questions.Seven genome-wide Perturb-seq campaigns across 16 contexts and 25 million filtered cells enabled X-Cell to transfer predictions from resting to activated T cells, held-out iPSC types, and primary donors, while patient, organ, and longitudinal dynamics remain unresolved.

Andy Beam × Rafa Gómez-Bombarelli

Lila is betting that controlled experiments can become AI’s next internet-scale training corpus, with nature providing verifiable rewards and an information-gain-driven lab turning experiments into a compounding model moat.Lila reports a six-month, two- or three-person in vivo CAR-T program versus roughly six years and $100 million, but clinical translation, scale-up, regulation, and 5–6% model FLOPs utilization remain key watchpoints.

swyx × Vibhu × Akshat Bubna

Modal is repositioning infrastructure around agent experience, using decorators, CLI observability, and a 17-provider footprint instead of owning data centers.Its investment case rests on bursty workloads and elastic orchestration: speculative decoding may deliver 2× to 4× speedups, while batch pricing and reliability determine whether compute planning converts into margins.

Brandon Anderson × Evan Feinberg × Sergey Edunov

Genesis Molecular AI is applying diffusion and inference-time scaling to 3D molecular design, targeting roughly 1 Å protein-ligand accuracy because 2 Å can hide chemically fatal errors.Its prospective edge comes from Incyte and Insitro programs that connect models to synthesis and ADMET feedback, while OpenBind showed stronger unseen-target generalization and GPUs remain the scaling bottleneck.

swyx × Matei Zaharia × Reynold Xin

Databricks is betting that an open agent layer can absorb model and harness churn while proprietary data, governance, and execution become the durable enterprise advantage.OmniGen, contextual policies, and LTAP target collaboration, security, cost exposure, and CDC friction, while specialized models—roughly 100× cheaper for document parsing—show where focused economics may beat frontier generality.

swyx × Zico Kolter × Matt Fredrikson

Gray Swan is positioning AI security as a separate control layer for untrusted models and agents, with SHADE finding more breaks than human red teamers in bounded tests while Zico Kolter cautions that superhuman red teaming has not arrived.Cygnal’s runtime policy enforcement could connect automated assessment, mitigation and AI insurance, but OpenClaw’s broad permissions show why isolation, authentication and narrow access controls remain necessary.