In this episode, Dai Yusen, partner at Zengge Fund Management, discusses the rapid evolution of the AI model landscape over the past six to eight months, the critical distinction between models and “harnesses” (agentic products), the unresolved return-on-investment question for massive token expenditures, and the emerging opportunities for AI-native startups. He emphasizes that while coding has become a horizontal enabler, innovation and product discovery remain the true bottlenecks. He also explores organizational changes needed for AI adoption, the rise of agent-to-agent marketplaces, and the long-term trajectory toward an AI-native digital world.
Key Points
Model landscape shifts quickly: OpenAI, Google, Anthropic, and others have traded leadership in coding, revenue, and user growth multiple times in 2025.
“Strong opinion, weakly held” – adapt views when underlying reasons change; market feedback is like reinforcement learning.
OpenAI’s revenue pieces: subscription growth has slowed; advertising progress has been slower than expected; corporate coding revenue surged unexpectedly due to agentic coding advances.
Anthropic’s strength in coding: coding was not an initial intention but emerged as training data included more code. Anthropic’s top-down organization (e.g., Dario Amodei’s biweekly memos) contrasts with OpenAI’s angel-investor-like resource allocation.
Harness vs. model: a powerful model alone is insufficient; a long-term harness (like Cloud Code, Codex, OpenCore) collects high-quality user data, enabling a data flywheel. Harnesses can be built by non-model companies.
Return question unresolved: massive AI hardware profits ($7T this year) and token spending (Antelope AR $1B/month) require real profits; payback may take years, and many companies have already reduced token usage.
Coding is horizontal, not vertical – it strengthens office work, medical work, and research. The bottleneck is innovation, not programming capacity.
Agent era metrics: shift from “attention is all you need” (DAU) to “attention is not all you need” – agents liberate human attention; key metric is how long an agent can complete valuable tasks.
Ecosystem evolution: three stages – make agents for humans, adapt agents to the human digital world, build a native digital world for agents.
Organizational change required: like the shift from steam engine shaft to electric wires enabling factory flow lines, AI demands organizational restructuring, not just tool adoption.
Investment focus: invest in excellent entrepreneurs (small genius, old driver, scientist, supermodel) before trends become obvious; favor first-tier companies with differentiated views.
New Labs are popular but not universally favored; China is hardware-focused (robots, world models) while US sees more research-oriented New Labs.
Deliberate practice with AI: start with simple projects to build proficiency; outsource execution but not understanding.
Concepts
Harness: An agentic product that adds context, tools, memory, and agent loops around a base model. Examples: Cloud Code, Codex, OpenCore, Manus, GenSpark. The harness becomes the OS, the model the processor.
Agent (Agenda): An autonomous entity that can be given long-duration tasks without requiring human attention; the main form of AI value delivery in 2025 and beyond.
Return question: The unresolved challenge of whether the massive capital expenditure on AI tokens (hardware and inference) will yield real profits for end customers, not just model providers.
In-distribution vs. out-of-distribution (OOD): AI excels at solving problems within the distribution of existing human data (e.g., coding, writing). Truly novel (OOD) creation – like original jokes or new mathematical theories – remains a human strength.
New Lab: A specially funded, free-form research organization focused on exploratory AI research, often separated from existing model company structures.
Details
Model Shifts and Revenue Dynamics
In the past six to eight months, model leadership has oscillated: OpenAI dominated in November/December (DAO ~$800M–$1B), Google’s live model emerged in December, Cloud’s coding ability peaked in January, Zopic’s (likely Claude Opus) revenue surpassed OpenAI in March, and by May Codex had more new users than Cloud Codes, though GPT‑5.5 also showed strength. Dai Yusen’s earlier prediction that OpenAI’s revenue would decline was partially accurate (subscription growth slowed, advertising was slower than expected) but wrong about corporate coding revenue, which grew significantly because of agentic coding advances enabled by Cloud 4.5 and 4.6. He subsequently adjusted his portfolio, adding storage, CPU, and bottleneck hardware investments.
Anthropic vs. OpenAI Organization
Anthropic is relatively closed to China; most information is second-hand. Its organization is top-down: Dario Amodei issues a thinking memo every two weeks, and every interviewee undergoes a value‑aligned interview. This contrasts with OpenAI, where many celebrity researchers pursue individual directions and resource allocation resembles an angel investor (Sam Altman, former YC president). Anthropic’s consistent direction helps it “control sand and bury” (post‑release maintenance), whereas OpenAI products often lack post-release protection. Yusen notes that Anthropic’s choice of coding as a specialty was not intentional – coding performance improved naturally as training data included more code.
The Harness Becomes the OS
Cloud Code, Codex, Minus, OpenCore, and Hermes are all “harnesses” that wrap a model with context, tools, agent loops, memory, and guardrails. The model is the processor; the harness is the operating system. Users can plug different models into the same harness (like Intel vs AMD CPUs). Brand lock-in is strong: users invest heavily in configuring tools like Cloud Code, reducing motivation to switch. Codex has engaged in a price war, offering similar capabilities at ~50% lower cost than Cloud Code’s equivalent tier, but GPT‑5.5 narrowed the gap. OpenCloud innovations include running on Mac, accessing files/calendar, a heartbeat mechanism, and memory MD (single context organized daily). It lives inside familiar IMs (WeChat, WhatsApp, Discord), a key reason for its popularity.
Open Source and Innovation
OpenCloud exemplifies how open source can attract users and later monetize via paid versions. Security concerns (tied to computer, reading files) make big companies cautious, but startups can “move fast and break things.” The harness space also allows non-model companies to innovate – e.g., OpenCloud, Manas, and others were initially dismissed as “shells” but are now considered critical.
Return Question and Token Economics
The real chain is input (tokens) → output (software) → result (profit, revenue increase, or cost reduction). Current investment assumes results will materialize, but the link is unproven. Simply multiplying engineering capacity (e.g., ten times more engineers via AI coding) does not automatically increase revenue; the bottleneck is knowing what to build. Many companies that burned large token volumes in March have already reduced usage, suggesting failed value delivery. Hyperscalers like Amazon raise capital for data centers, shortening the time demand for answers. Antelope’s AR is expected to reach $1 billion per month by year-end, meaning $10 billion spent on tokens must eventually justify returns.
Coding as a Horizontal Field
Coding is not a vertical like medical or finance – it strengthens all fields. High-quality user coding data makes models better at programming. Agentic loops that previously could not run now can, enabling longer-term, higher-value tasks. Even for non-programming office work, AI can generate more output but does not replace human responsibility for decisions (e.g., investment decisions still require a person to bear the loss).
Organizational Impacts
New companies (e.g., Slack, Cloud Code, Codex) are built from day one with AI participating in management and software development, blurring divisions between front‑end, back‑end, UI, and testing. Established companies struggle because their context and data are not visible to AI; pushing AI requires organizational change, not just installing a tool. The industrial revolution analogy: steam engines were organized around a central shaft; electric engines allowed flat factories and Ford’s flow line. AI likewise demands human organizational change that may take a decade.
Agent-to-Agent Marketplaces
As agents accumulate context, skills, and personalization, they become differentiated. This enables value exchange: e.g., my agent hires Zhang Xiaojun’s agent for proprietary interview prep knowledge. Payment split: ~1000 yuan for token cost, ~9000 yuan for exclusive knowledge. This creates an agent-to-agent marketplace similar to e-commerce. Examples: Guizhang’s PPT skill is sold via his agent; an “agent-generated marketplace” where agents autonomously run long-horizon tasks.
Investment Philosophy
Dai Yusen’s firm invested in ~100 projects last year, selecting two; by May this year, three investments. They target excellent entrepreneurs (learning ability, leadership, creativity, willpower) before fields become trendy. They followed two young Tsinghua PhDs (Liu Songming, Dingling) for years because of outstanding academic work; later those founders started companies in robot brains and world models. The firm aims to become the first‑round largest investor. Founder types include small genius, old driver, scientist, and supermodel. Key criteria: early adoption, deep technology understanding, original judgment.
Current Hot Areas
In the VC community, three topics have become very popular over the past six months: world models, coding, and auto research (AI self-adaptation and self-improvement). China is much more hardware-focused (robots, AI hardware, commercial rockets) with about 10 commercial rocket companies. The “world model” concept is hot but contested – definitions vary widely. Egocentric video data (hands doing tasks) is training models; multiple routes exist for robots, some paralleling language model scaling.
Advice for Entrepreneurs and Individuals
Build horizontal (general-purpose) products rather than vertical ones in early technology development, because vertical can trap you as technology shifts. Examples of horizontal platforms: Poverty, Manus, GenSpark, OpenCloud, Hermes.
Start with simple AI projects (sync meetings, build monitoring tools) as deliberate practice, like fitness training.
The ability to create never‑before‑seen content (out‑of‑distribution) is hard to replace; focus on innovation, curiosity, and human interaction requiring trust and responsibility.
Society will need to address unemployment through UBI or tax redistribution; new work will emerge as with the industrial revolution, but the pace is faster.
Personal Reflection
Dai Yusen uses AI daily to organize information (blog, newsletter, meeting notes) and tracks people, companies, and stocks. He cautions that outsourcing thinking can weaken understanding; deliberate practice (e.g., preparing a book without AI summarization) is necessary. His friend Mr. Lin deliberately practices AI innovation by building projects like a domain‑name scanner, a cat monitoring system, and a health‑data dashboard – all with no direct value but teaching agent collaboration. Getting “hit in the face” by the market is a source of happiness for early investors; summarising thoughts clarifies understanding and reveals confusion.
