IntelliPharma Insights

Claude Science, slow real-world feedback, cascade control, information gain, and Kauffman’s open phase space offer a framework for thinking about evolving scientific intelligence.

Starting from China’s innovative-drug licensing wave, quasi-market design, reimbursement reform, and capital-market transition, this essay examines the development stage of Chinese pharma and the capabilities that companies and domestic investors must preserve.

Using general-purpose technology theory, the productivity J-curve, and capital appropriability, this essay explains why AI clusters in digitally closed spaces—and how AI drug discovery can cross the capital gap in physically and organizationally open systems.

Beginning with the Lilly–Insilico collaboration, this essay explains why the value of an AI drug discovery company cannot be reduced to the success rate of a single pipeline—and how capital sentiment, organizational capability, and long-term technological change shape valuation.

Beginning with the OpenClaw craze, control theory, and China’s open-source history, this essay examines the capital, governance, and technology tensions shaping AI-powered drug discovery.

Through AlphaZero, self-play, tool-using agents, and reward hacking, this essay explains why closed-world reinforcement learning cannot directly overcome the sparse true rewards of drug development.

Through metascience, the science of science, and automated research systems, this essay examines the predictable combinations, unpredictable breakthroughs, and engineerable conditions of pharmaceutical innovation.

From Codex App Server to LUMI-lab, this essay explains why software engineering evolves quickly under AI and how the pattern may spread.

A New Year reflection on technology, creativity, economic thought, and the deliberate choice to remain optimistic about the future.

Why de novo protein design remains constrained in biomedicine—and how new engineering domains and biological ontologies could unlock its potential.

Deep dive into AI Agent's impact on biopharma industry, keywords include: Manus, Agent, process liberation, production relations.

Deep dive into the bargaining power logic of AI+pharma product companies, keywords include: bargaining power, network leverage, capital leverage, competitive landscape.

Deep dive into AI platform cost economics and transaction cost theory, keywords include: AI Agent, transaction cost, complexity allocation, firm boundaries.

A look back at NL-201, Neoleukin, and what de novo protein design teaches about clinical validation.

A systems-engineering view of AI drug discovery, contrasting hard systems with softer biological and organizational realities.

An essay on whether AI drug discovery companies are selling tools, platforms, services, or productized therapeutic capability.

An essay on why AI drug discovery needs organizational and industrial culture change, not only better models.

A reflection on undruggable targets, newly discovered pockets, and why AI drug discovery should return to therapy-level innovation.

A practical discussion of AI drug discovery pipeline valuation, from rNPV models to business development transaction logic.

From AI capital cycles to durable value, this essay asks how to think about AI drug discovery beyond the bubble.

A historical and theoretical reflection on human-computer interaction, AI-native UX, and how expertise changes in the AI era.

What Lilly's Blackwell supercomputer reveals about big pharma's AI strategy—and why better models may be sustaining rather than disruptive innovation.

Explore the AI/IT department structure in AI-driven pharmaceutical industry, with keywords including: DevOps Three Ways, Learning Organization, Complexity Revolution.

Explore the system complexity revolution and native organizational forms in the AI era, with keywords including: Man-Month Myth, AI-Native Organization, System Complexity, Productivity Innovation.

A reflection on vibe coding, spec coding, and how AI changes the asset structure of software and industry workflows.

An analysis of Eli Lilly's AI strategy, from external partnerships to internal capability building and ecosystem design.

A reflection on hallucination, AlphaGo, and whether AI-generated deviations can expand scientific experience rather than merely create errors.

A discussion of protein docking in real antibody development contexts, including Ab-Ag docking, structure assembly, and binding dynamics.

A discussion of why agents and workflows are not opposing paradigms, but complementary layers in reliable AI application architecture.

A proposal for research systems designed around AI strengths, with coding agents and deep research agents as reference cases.

How Chinese biotechs can differentiate in the crowded GLP-1 market through comorbidity strategies, muscle preservation, and better product design.

What China's food-delivery subsidy war reveals about destructive competition in pharma—and how AIDD could shift competition toward genuine productivity.

How Novo Nordisk's first-mover advantage collided with supply constraints—and how Lilly used dual agonism and manufacturing capacity to catch up.

How Figma, Brooks's Law, and loose coupling reveal a practical architecture for AI-enabled Holistic Drug Development.

How Chai-2 exposes the plasmid-library bottleneck in antibody design—and why generative models must converge with experimental screening.

What Palantir's Ontology and project-delivery model can teach AIDD companies about commercializing AI in a capital-intensive industry.

Starting from the semantic gap between drug and medicine, this essay asks how AIDD can move beyond model-performance narratives and return to clinically unmet needs.