AI and Emerging Technology ── Recording how AI changes, from the front line
A journal recording the daily developments in the world of AI, viewed from the pharmaceutical field. Model updates, corporate moves, regulatory shifts, industry numbers ── only articles that have passed verification are placed here.
Can AI Move from Lab Assistant to Scientist? ── A Cell perspective traces the rise of agentic AI in hypothesis generation, experiment design, and iterative reasoning
A peer-reviewed perspective published in Cell examines three agentic AI systems — Co-Scientist, Robin, and Biomni — that autonomously generate hypotheses, design experiments, and refine reasoning through feedback. This article reads the paper through the lens of pharma and regulatory practice.
Read more →Bridging the Gap Between 'It Works on a Benchmark' and 'It's Safe in a Clinic' ── Reading the phase-based evidence standards framework published in NEJM AI
A peer-reviewed perspective in NEJM AI proposes a phase-based framework for evidence standards in clinical AI, mapping the journey from technical performance metrics to clinical readiness. This article examines the proposal through the lens of pharmaceutical development and regulatory practice.
Read more →Cutting a Coding Agent's 'Fuel Cost' in Half ── SoL-Pi proposes recursive self-improvement at the harness layer to slash token consumption
SoL-Pi, a preprint from NVlabs and collaborators, reports halving token consumption for coding agents while maintaining performance by recursively improving the harness layer. This article reads the preprint within the scope of its abstract.
Read more →Can a Foundation Model for Tabular Data Learn Causality? ── What LimiX-2's shift from prediction to joint modeling means, and where the limits are
LimiX-2, a new foundation model for structured data, replaces the conventional 'predict y from x' objective with joint modeling of p(x, y | context). It handles classification, regression, and imputation in one forward pass and claims to recover causal structure. We review the preprint within the bounds of its abstract.
Read more →Game AI Has Moved Beyond Playing to Building, Testing, and Adapting ── A large-scale survey maps six roles for AI across the game lifecycle
Game AI used to mean beating humans at chess or Go. Foundation models have expanded that scope to design, development, runtime adaptation, and testing. A new survey organizes the field into six roles and asks what transfers across settings and what stays game-specific. We review the preprint within the bounds of its abstract.
Read more →How Far Has AI-Assisted Intraoperative Pathology Come? ── Reading the peer-reviewed CRISP paper, a foundation model trained on over 100,000 frozen sections
Published in Nature Medicine, CRISP is a foundation model for intraoperative pathology built from over 100,000 frozen sections across ten centers. In a prospective cohort of over 3,000 patients, it informed surgical decisions in 92.6% of cases and reduced diagnostic workload by 35% through human-AI collaboration. A review from the pharmaceutical and regulatory perspective.
Read more →Replaying Past Discoveries as Dreams to Refine Exploration ── Dream-RSI's proposal for off-policy self-improvement through discovery history
How can an AI agent continuously improve its own exploration strategy? Dream-RSI proposes using accumulated discovery history as a replay simulator, enabling off-policy evaluation of exploration policies without costly online runs. A review of this preprint on recursive self-improvement.
Read more →Giving a Language Model a Body ── PhysBrain 1.5 attempts to bridge vision-language models and physical foundation models
PhysBrain 1.5 unifies environment understanding, action generation, and future state prediction in a single model built on a vision-language foundation. Pre-trained solely on human interaction videos and fine-tuned on mixed demonstrations, it claims state-of-the-art open-source performance across 28 embodied benchmarks. A review of this preprint.
Read more →Why the U.S. Department of Energy Is Building Its Own AI for Science ── The Genesis Mission and the Open-Weight Bet
The U.S. Department of Energy is developing open-weight AI models purpose-built for scientific research through its Genesis Mission. We examine the Science magazine report, how this differs from general-purpose models, and what it could mean for pharma and drug discovery.
Read more →Repurposing Image Generation Models for Depth Estimation ── What Marigold V2 reveals about diffusion transformers beyond image synthesis
Marigold V2 converts a diffusion transformer trained for image generation into a single-step monocular depth estimator using semantic alignment and a Sinkhorn-based two-stage fine-tuning protocol. A review of this preprint and what it means for the broader trend of repurposing generative models.
Read more →Letting a VLM Play the Robot ── Show-Harness bridges foundation vision-language models to robot control through a semantic action interface
Show-Harness proposes a compact semantic interface that lets vision-language models control robots via discrete action units, enabling zero-shot deployment of closed-source VLMs and low-cost fine-tuning of small open-source models on the same design.
Read more →Miles v0.1: A Full-Stack System for Production-Grade Post-Training at Frontier Scale ── RadixArk's open-source framework integrates SGLang, Megatron-LM, and FSDP under a single design principle
Miles v0.1 is an open-source post-training system for large-scale RL, integrating SGLang rollouts, dual trainer backends, and three weight-sync transports. Preprint, not peer-reviewed.
Read more →Gander: An Omni Interaction Agent Beyond Turn-Based Dialogue ── A Cerebellum-Brain framework and streaming Thinker-Talker architecture for full-duplex multimodal interaction
Preprint arXiv:2609.08977. Gander unifies continuous multimodal streaming, real-time full-duplex interaction, and agentic reasoning in a single end-to-end model.
Read more →Learning from What You Can Do ── Toward recursive self-improvement in agent-native models
NeoHorse-1 embodies a concrete mechanism for recursive self-improvement, where an agent-native model observes its own capabilities and uses that observation to direct the next round of learning. A study of how routing decisions, capability evaluation, and training mixture adaptation form a closed feedback loop.
Read more →Making and Editing Speech in the Same Language ── Tencent's open-source AuK unifies speech generation and editing through natural-language instructions and audio context
AuK (Technical Report, arXiv: 2609.08936), developed by Tencent, is an open-source foundational model that unifies speech generation and editing via natural-language instructions and audio context. A preprint not yet subject to peer review.
Read more →Making Robotics Reason About Worlds ── Design principles for modular world-action models
OpenWAM presents an open research stack that decomposes world-action model design into testable components. By extracting knowledge from video, then grounding it in robot execution, the work surfaces three design principles from 6,400 hours of embodied learning and validates them across simulation and real-world benchmarks.
Read more →Teaching AI Agents the Tricks of the Trade ── DisCo distills operational know-how from GitHub repositories into reusable skills
A preprint proposes DisCo, a research agent that distills operational knowledge from ML repositories into reusable skills, yielding the 5,000-skill AREX-Skill Library and large benchmark gains.
Read more →A Common Foundation for Video World Models ── SolarWM unifies data, training, and inference across backbones
SolarWM proposes an open framework that unifies heterogeneous video datasets and generator backbones into a reproducible training pipeline for interactive world models. A preprint review.
Read more →Random Eviction Works Just as Well ── Random Attention challenges the need for scoring in KV cache compression
A preprint shows that randomly evicting reasoning tokens from the KV cache matches the best scoring-based methods while delivering 32-43% higher throughput. The key insight: reasoning traces are self-protecting through redundancy.
Read more →Anthropic Moves Closer to Drug Discovery ── Claude Science Launch and In-House Program Mark a Turning Point
On July 1, 2026, Anthropic (= the US AI (artificial intelligence) company that develops Claude) took a step beyond simply selling research tools. On the same day, it formally launched "Claude Science," a workbench for scientists (= a workspace where research tasks such as experiment planning and literature review are handled on a single screen), and started its own drug discovery program in the biopharma field. Entry into the drug development business itself, however, is reported to be "under consideration" (The Verge, July 4). Three months have passed since the CEO of Novartis (a major Swiss pharmaceutical company) joined Anthropic's board in April. The boundary between tool provider and pharmaceutical company is shifting.
Read more →The Invisible Line Drawn by Pay-Per-Use Frontier AI ── When only those who can pay touch the best intelligence, what should pharma prepare for?
Pay-per-use looks fair, but with frontier AI it means those who pay more get deeper thinking. Using the Fable 5 generation, we trace how this differs from electricity and cloud history, the two layers of inequality — opportunity and amplification — the gap in trial counts between large and small firms, and the changes possible in materials creation, review, and medical affairs. We also weigh the leveling power of distillation and open models, and the conditions under which this reading fails.
Read more →The Released Emails Showed Why the Talks Collapsed. Anthropic Refused the Pentagon's Demands, While OpenAI Moved Closer to the Administration ── On July 2, 2026, negotiation emails between Anthropic and the Pentagon (the U.S. Department of Defense) were released as court filings. The same day, OpenAI was reported to have offered the U.S. government a 5% stake in the company. The two stories give opposite answers to the same question: how should an AI company deal with the military and the government?
Anthropic tried to keep one condition in its military contract — no use for autonomous weapons or domestic surveillance — and lost a $200 million deal and the government's goodwill. OpenAI went the other way, even offering the government a 5% stake in the company. Using the released emails as a guide, we trace where an AI company's principles and its business interests part ways.
Read more →Selling the Ceiling and the Floor at Once ── Show the limits of capability with the top model, widen everyday adoption with the workhorse. Reading the meaning behind a same-day release.
On July 1, 2026, Sonnet 5's launch and Fable 5's return after export controls lifted fell on the same day, giving Anthropic's shelf both a capability ceiling and an adoption floor. We map this two-tier pattern, familiar from chips and cars, onto AI pricing, contrast OpenAI and Google, and show pharma readers how to sort work into volume and top bands by failure cost.
Read more →The Day the Marginal Cost of Knowledge Work Approaches Zero ── Extrapolating the pace of model generations from Opus 4.7 to Fable 5: how far could organizations, jobs, education, and geography change in 2027–2029?
Measuring the model generations from Opus 4.7 to Fable 5 as a slope of agentic capability, this piece extrapolates 2027–2029 in order of likelihood: middle management shrinks as an information relay, junior careers start with checking rather than making, and credentials shift from proving knowledge to accepting responsibility. Three failure conditions — regulation, power, and social adoption — are stated up front.
Read more →Reading the 2027 Job Listings in Advance ── Verifiers, context suppliers, and go-betweens — the shape of new work in pharma review, medical affairs, and safety
Not a story about AI erasing jobs, but a forecast of the jobs born at the AI-human boundary. Agentic AI shifts the human role toward commissioning, verifying, and taking responsibility. Set in pharma — where errors are costly and responsibility is assigned by law — three lineages are extrapolated to 2027–2029: AI output auditors, context suppliers who translate in-house knowledge, and go-betweens linking AI to regulation. Qualifications come after occupations; the preparation you can start today is putting your decision process into words.
Read more →The Day Knowledge Gets Cheap: 2027–2029, Value Shifts from "Knowing" to "Answering For It" ── Reading the slope of model progress: where trust, authenticity, and conscience belong in the AI era
Generative AI has slashed the cost of producing text, code, and knowledge — and what becomes abundant becomes cheap. So what will people pay for, and trust, in 2027–2029? Drawing on how handcrafts were re-valued after industrialization, this essay traces the shift toward certainty over speed, context over volume, and the authenticity of "a human answered for this" — including what it means for pharmaceutical material review and MR visits, and the conditions under which the forecast could miss.
Read more →The Day AI Designed a Burger
I was walking home after clinic when a paper alert came through on my phone and stopped me in my tracks. "Ge…
Read more →The Art of Breaking Assumptions
This issue examines first principles thinking, a concept Elon Musk returns to repeatedly. Most discussions f…
Read more →The End of Slide-Making as Work
In this issue, I take McKinsey's AI transformation as a starting point for examining what it means to "make …
Read more →How Fable Will Drive the Next Generation of AI Development
How Fable will shape the next generation of AI development — read through three layers: architecture, evaluation metrics, and regulatory design.
Read more →How the US Government Forced Fable into a Temporary Halt
The US government's response to the AI model Fable — the multi-layered regulatory, political, and technical chain that led to the temporary halt.
Read more →The True Identity of Anthropic Fable 5.0 — The Name on the Surface and What Lies Beneath
What is Anthropic Fable 5.0? The name on the surface and the technological, organizational, and regulatory structures behind it.
Read more →Lessons from History — From Stagecoach to Railway, Windows, and AI
The history of technological transformations — stagecoach to railway, automobile, Windows, internet. The judgment structure of "ride" or "leave" in the AI era.
Read more →At the Threshold of Intelligence Explosion — How Society Will Change, How to Prepare
Signs of an "intelligence explosion" pointed to by recent AI trends. Structural changes in society and what to prepare for as pharma professionals.
Read more →The "New Intelligence" in the AI Era — A Five-Layer Structure of Literacy
What is the "new intelligence" demanded by the AI era? A five-layer model of literacy and the diverging points where society splits, examined from the pharmaceutical field.
Read more →AI Reshapes the Social Structure — A 2024-2025 Layoff Retrospective and 2026-2028 Outlook
Over 240,000 tech-industry layoffs in 2024-2025 as AI took hold. A structured retrospective and a forward look to 2026-2028, when AI agents go mainstream and 50-55% of roles get redesigned. Implications for pharma.
Read more →Novartis CEO Joins Anthropic's Board — A Symbol of AI–Pharma Strategic Fusion
April 2026: Vas Narasimhan joins Anthropic's Board via the Long-Term Benefit Trust. The essential meaning of "responsible AI" directly connecting with pharma, and two implications for the industry.
Read more →AI Drug Discovery, the Last Year — "The Year of Clinical Validation"
173+ AI-derived candidates in clinical trials, early-Phase I success at 80-90%, Insilico's rentosertib clears Phase IIa as first generative-AI-designed drug. Pfizer-Boltz, Recursion × Exscientia, AlphaFold3's 1000× speedup.
Read more →AI Clinical Practice in the Middle East and China — Saudi's "Dr Hua" and China's 300 Models
Saudi Arabia's world-first AI doctor clinic, UAE Burjeel × Hippocratic AI, China's 300 medical AI models and 68 million remote diagnoses. Why emerging markets are leading AI clinical deployment.
Read more →Is AI Just the World of Chatbots? — What Humanoid Spread Means
AI = chatbot? Only a fraction. China-led humanoid robots are spreading explosively, taking real work in factories and warehouses. What it means when AI is released into the physical world.
Read more →China's "AI Plus" Plan, and Society Already Changing
State Council of China's August 2025 "AI Plus" Action Plan. A national strategy to integrate AI across society, reshaping industry, daily life, and governance. 86% generative AI usage, 1.2 trillion yuan industry — read in numbers.
Read more →Microsoft unveils 7 AI models in one shot — plus the Scout autonomous agent
Microsoft just released 7 in-house AI models including the reasoning-focused MAI-Thinking-1, and launched the Scout autonomous agent built on OpenClaw — a clear signal that the company is starting to distance itself from OpenAI.
Read more →Stanford AI Index 2026 — Five Numbers That Show AI Becoming Infrastructure
Adoption faster than PC or internet, US-China model gap under 1%, $172B in enterprise value, data centers eating country-scale power, and edge AI going mainstream. The five numbers that mark AI's shift from technology to infrastructure.
Read more →Agentic AI 2026 ── From "AI that answers" to "AI that works"
The era of AI that "just responds" is ending. With Anthropic Computer Use, Google Gemini Spark, and NVIDIA local AI chips all arriving, 2026 marks the turning point at which AI becomes a worker. A high-school-readable look at the rookie-to-mid-career "promotion" happening now.
Read more →NVIDIA "RTX Spark" ── The day AI moves into your PC
The new superchip unveiled at NVIDIA GTC Taipei on June 1. The starting line of an era where ChatGPT-class AI runs locally on your PC, no cloud needed. Using a "Uber Eats vs. cooking at home" metaphor, we read what it means for the pharmaceutical field.
Read more →Who is the Anthropic "mythos"? ── The story of the AI company most afraid of itself
Claude, the ChatGPT rival, is made by Anthropic. They publicly declare "We're the most worried company about AI, for humanity's sake," and yet they sell some of the world's strongest AI. Where does that contradiction come from? A relaxed, high-school-readable explainer.
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