张小珺Jùn｜商业访谈录

Investors should prioritize Big Tech leaders like Microsoft (MSFT), Alphabet (GOOGL), and NVIDIA (NVDA) as they provide the essential "frontier models" and hardware for the shift from AI tools to autonomous "AI Labor." Focus on Anthropic (via Amazon or Google partnerships) for its leadership in "Computer Use" technology, which allows agents to automate legacy software without complex integrations. Look for high-conviction opportunities in Vertical AI startups that solve "hard problems" in legal, medical, or coding sectors, as these specialized "Expert Agents" maintain a stronger competitive moat than general chat models. Monitor the 2025-2026 timeframe for the commercial rollout of "Self-Learning" agents, which is expected to significantly optimize enterprise business processes and high-end knowledge work. Be cautious of "simple" AI startups, as general models like GPT-4o and Claude 3.5 are rapidly absorbing basic features and displacing under-capitalized competitors.

The next 2–3 months represent a critical window for the transition from simple chatbots to AI Agents, with value shifting toward framework layers like OpenClaw and Llama. Investors should prioritize companies utilizing efficient architectures like MTP and MIA (e.g., DeepSeek and Xiaomi), as these "cost-performance" leaders are narrowing the gap with US models to just 2–3 months. Expect a 10x surge in demand for inference-specific hardware as persistent agents begin scanning screens and performing long-term tasks. The most reliable ROI over the next 24 months will likely come from AI for Software Engineering, as coding provides the most stable environment for AI self-evolution. Avoid AI applications in sectors with "messy" feedback loops, such as certain quantitative finance models, where the lack of clear rewards hinders model training.

Investors should monitor Google (GOOGL) and Amazon (AMZN) as they lead the transition from "probabilistic" AI to "deterministic" reasoning through projects like AlphaProof and AWS automated reasoning teams. High-conviction opportunities are emerging in Vertical AI startups that focus on Formal Verification and the Lean programming language to eliminate AI hallucinations in software and chip design. While Axiom is currently a private unicorn, its "SpaceX-like" risk profile suggests a binary investment outcome: either achieving super-intelligence in mathematics or total failure. For 2025-2026, look for specialized labs that prioritize Reinforcement Learning and Post-training over expensive pre-training, as these models offer a more capital-efficient path to market. The most actionable shift for the next two years is toward Verified Code Generation, where AI provides mathematically proven, bug-free code rather than simple suggestions.

Investors should prioritize Anthropic (private) as the current leader in high-value coding agents, with the company projected to reach over $2 billion in ARR next year. While OpenAI (private) temporarily lost focus, watch for the release of their "Sparrow" model as the critical catalyst for them to reclaim the lead in agentic workflows. For a stable, long-term play, Google (GOOGL) remains a high-conviction "safe bet" due to its vertical integration with proprietary TPU hardware and massive distribution via Workspace. High-growth specialized startups like Manus (agents), Eleven Labs (voice), and Perplexity (search) offer significant upside as they capture specific niches of the AI economy. A balanced AGI Portfolio should allocate 20% to top-tier models like the "Big Three," 10% to agent infrastructure, and 10% to AI-driven science.

Investors should prioritize exposure to OpenAI and Meta, as their massive data moats position them to dominate the transition from AI "tools" to "personal agents" that act as digital proxies. Look for opportunities in the emerging AI-native social networking sector, which is disrupting a 15-year period of stagnation by using "context-driven" models to automate social friction and networking. High-conviction value lies in major IP holders like Disney and Nintendo, who can leverage deep character lore to create high-margin, interactive "digital souls" for fans. Monitor the Shenzhen tech hub for early-stage startups like Natural Selection, which are utilizing local AI talent to build "cyber-doppelgangers" that manage personal branding and information filtering. Be cautious of "pixel-level" copycats and prioritize companies whose competitive advantage is built on proprietary user data and "context flow" rather than just interface design.

Investors should prioritize NVIDIA (NVDA) as it cements its role as the essential infrastructure provider for autonomous driving and robotics through its Orin chips and Omniverse simulation platform. Meta Platforms (META) offers a unique data-collection advantage via its Ray-Ban smart glasses, which serve as a "Trojan Horse" to capture the first-person physical world data necessary for training future AI agents. While Tesla (TSLA) remains a leader in data scaling through its massive vehicle fleet, the industry is shifting toward "Embodied AI" and Vision-Language-Action (VLA) models, making specialized robotics labs and "Data Infrastructure" firms high-conviction themes. For exposure to the hardware side of this transition, monitor Chinese manufacturers like Xiaomi and Xpeng, which are leveraging their supply chains to lead in the physical deployment of humanoid robotics. Focus on companies that prioritize "AI Education" and high-quality synthetic data, as the ability to simulate and evaluate complex physical tasks is the next major bottleneck for the industry.

Investors should prioritize Physical AI and World Models as the next major frontier beyond current text-based LLMs, focusing on companies that bridge the gap between digital intelligence and the physical world. Keep a close watch on AMI Labs, a high-conviction startup co-founded by Yann LeCun, which is currently raising capital at a $1 billion valuation to build "Predictive Brains" for robotics and healthcare. While Meta (META) remains a talent powerhouse, be aware of potential "brain drain" as top researchers leave to join agile startups focused on fundamental spatial intelligence. OpenAI continues to lead in product execution, specifically through the Diffusion Transformer (DiT) architecture used in Sora, which has now become the industry standard for video generation. For long-term growth, look toward Robotics and Industrial AI firms that own proprietary sensor and video data, as high-quality visual data will be the most valuable resource for training the next generation of autonomous systems.

To invest in the future of autonomous driving, consider either the integrated car company Tesla (TSLA) or the core technology supplier NVIDIA (NVDA). The investment thesis for Tesla is based on its AI-native approach and its massive data advantage from its vehicle fleet, which is crucial for developing Full Self-Driving. Alternatively, NVIDIA represents a "picks and shovels" investment, supplying the essential high-performance computing hardware to nearly all major players in the autonomous vehicle sector. Investing in Waymo through its parent company Alphabet (GOOGL) is a much longer-term bet on the robotaxi model, which faces significant operational hurdles. Therefore, NVDA offers broad exposure to the entire industry's growth, while TSLA is a higher-conviction bet on a specific company winning the AI race.

The Chinese autonomous driving market is expected to consolidate to just three major players within the next three years, with Huawei positioned as a dominant leader. Investors should therefore be cautious of smaller companies in this sector that may not survive this consolidation. The most promising long-term AI investments are companies that combine their models with hardware, as pure software plays are considered too risky for new entrants. Xiaomi is highlighted as a prime example of a company successfully executing this AI-plus-hardware strategy. Investors should also monitor Tesla (TSLA), as its approach to self-driving serves as a critical benchmark for the entire industry.

The next major investment opportunity is in AI Agents, which are poised to become the next computing platform by automating complex tasks. Meta (META) is strategically positioned to capitalize on this trend, as validated by its smart acquisition of an AI agent startup. Conversely, the rise of specialized AI agents presents a significant disruption risk to established software companies like Adobe (ADBE). When evaluating opportunities, prioritize companies building true "AI Native" products that create entirely new user experiences rather than just adding features. In the near term, watch the gaming sector for companies using AI to innovate on dynamic character development and storytelling.