Global Technology Opportunity Fund

运用 AI
投资 AI
Investing in AI
with AI

AI-Empowered, Human-Led Quantamental Hedge Fund

一只为 AI 时代设计的科技对冲基金:用人的前瞻抓住历史上没出现过的新物种,用量化纪律驾驭 AI 的强周期,用智能体投研团队跟上每周都在变化的产业。 A technology hedge fund built for the AI era: human foresight to catch what has never existed before, quantitative discipline to ride AI's violent cycles, and an AI-agent research team to keep pace with an industry that changes every week.

周期从哪来:认知差Where cycles come from: the perception gap
AI 实际进步:指数级AI progress: exponential 人的认知:线性Human perception: linear 认知差the gap 时间time
AI 实际进步Actual progress 人的认知Perception 股价:围着认知差大幅摆动Prices: swinging around the gap
示意图,非真实数据Illustrative, not actual data
增长引擎的 S 曲线:按阶段买卖The growth-engine S-curve: trade by stage
S0纯概念ConceptS1产品验证ValidatedS2业绩爆发InflectionS3持续兑现DeliveryS4引擎放缓SlowingS5行业卷Commoditized 买入区间Buy zone 卖出区间Sell zone
示意图,非真实数据Illustrative, not actual data
差异化Our Edge

选股、做多做空,所有对冲基金都在做。我们押注的是 AI 时代独有的东西。Every hedge fund picks stocks and goes long and short. We build for what only the AI era offers.

五个支点,共同构成 xResearch 的投资方式。Five pillars define how xResearch invests.

Alpha 来源Sources of Alpha

Alpha 从哪来:AI 的周期性,和 AI 带来的加速Where alpha comes from: AI's cyclicality, and the acceleration AI brings

来源一Source One

AI 行业的周期性The AI industry is deeply cyclical

技术进步是指数级的,人的认知是线性的——认知差不断放大,市场在过度乐观和过度悲观之间大幅摆动。Technology advances exponentially; human perception improves linearly. The gap keeps widening, and the market swings hard between over-optimism and over-pessimism.

14
费半 25 年里跌幅超 20% 的熊市SOX bear markets (−20%+) in 25 years
−38%
已结束 13 轮的平均最大跌幅Avg. max drawdown, 13 completed bears
+121%
熊市之后牛市的平均涨幅Avg. gain of the bull that followed
+311%
上一轮 AI 牛市的涨幅Gain of the last AI bull run
我们怎么赚How we capture it识别周期位置,按牛熊阶段调整仓位:底部敢加仓,顶部肯收手。Identify where we are in the cycle and size by bull/bear stage: add with conviction near bottoms, pull back near tops.
来源二Source Two

AI 带来科技进步的加速AI accelerates scientific progress itself

蒸汽机放大体力,电力放大能量,互联网放大信息传输——而 AI 复制的是研究员本身。工具让人做得更快;复制科学家,让“发现”本身变快。The steam engine amplified muscle, electricity amplified energy, the internet amplified information. AI replicates the researcher. Tools make people faster; replicating scientists makes discovery itself faster.

突破的数量Breakthroughs= 研究员数量Researchers× 能力Capability× 试错速度Iteration speed
2016
AlphaGo 第 37 手:人类棋手下出这步棋的概率约万分之一Move 37: a move a human would play with ~1-in-10,000 odds
2021–22
AlphaFold 人类实验约 50 年解出约 18 万个蛋白结构;AlphaFold 约 1 年预测了 2 亿多个~180k protein structures in 50 years of experiments; 200m+ predicted in about a year
2025–26
算法与数学难题Algorithms & math AlphaEvolve 打破保持 56 年的算法纪录;AI 推翻 80 年前的数学猜想AlphaEvolve breaks a 56-year-old algorithmic record; AI overturns an 80-year-old conjecture
2026
精准医疗Precision medicine 全球首个三期成功的 mRNA 抗癌疗法The first mRNA cancer therapy to succeed in Phase 3
我们怎么赚How we capture it看增长引擎选股:新物种、新环节、新赢家不断出现,业绩爆发时加仓,引擎放缓时退出。Pick stocks by growth engine: as new species, new links and new winners keep emerging, add when results inflect, exit when the engine slows.
选股、做多做空,所有对冲基金都在做;周期的摆幅和加速带来的新物种,是 AI 时代更大、也更可重复的 alpha。Every hedge fund picks stocks and goes long and short. The amplitude of the cycle and the new species born from acceleration are the larger, more repeatable alpha of the AI era.

统计:费城半导体指数(SOX)2000-01-04 至 2026-08-26 收盘价,双向 20% 划分;平均跌幅不含进行中的一轮。历史统计只来自有限样本,不代表未来表现。Source: Philadelphia Semiconductor Index (SOX) closes, 2000-01-04 to 2026-08-26, ±20% threshold; average drawdown excludes the round in progress. Historical statistics come from a limited sample and do not predict future results.

定位Positioning

AI 驱动的 Quantamental:既有量化的纪律,也有人的前瞻AI-driven Quantamental: quant discipline plus human foresight

主观多头Discretionary long
纯量化Pure quant
xResearch
洞察来源Insight source
个人经验与深度调研Personal experience & deep research
历史数据中的统计模式Statistical patterns in history
量化+基本面+主观前瞻Quant + fundamentals + human foresight
纪律与执行Discipline & execution
依赖基金经理个人状态Depends on the manager's state
强:系统化执行Strong, systematic execution
预注册规则,AI 监督执行Pre-registered rules, AI-supervised
没发生过的事The unprecedented
能识别,但常无纪律承接Can spot it, often no discipline to act
识别不了——只学过历史Can't see it: models only learn the past
人来预判,规则来承接Humans call it, rules carry it
研究覆盖Research coverage
受人力与精力限制Limited by human bandwidth
广,但基本面理解浅Broad, but shallow on fundamentals
AI 智能体 7×24 机构级覆盖AI agents, 24/7, institutional-grade

为什么恰好适配 AI 时代Why it fits the AI era

判断新事物,需要人Judging the new takes humansAI 时代全是“没见过的东西”,训练数据里没有。The AI era is full of things no training set has ever seen.
驾驭强周期,需要量化Riding strong cycles takes quant光有判断不够——位置感、历史统计、按规则执行。Judgment alone isn't enough: positioning, statistics, rules.
跟上快变化,需要监控Keeping up takes monitoring定性+定量的监控体系,逐周读数。Qualitative and quantitative monitoring, read every week.
新物种:历史上没有过New species: no precedent
AI 大模型AI foundation modelstoken 经济学没有历史样本——2022 年之前,不存在“卖智能”这门生意。Token economics has no historical sample: before 2022, “selling intelligence” wasn't a business.
商业航天与轨道算力Commercial space & orbital compute可复用火箭、卫星互联网、轨道算力,三样都无先例,连可比公司都难找。Reusable rockets, satellite internet, orbital compute: no precedent, barely any comparables.
Robotaxi从 0 到 1 的业务,处在最难定价的“纯概念 → 产品验证”阶段。A zero-to-one business in the hardest-to-price stage: concept → product validation.
规律改写:历史规律失效Rewritten rules: history stops working
存储的估值远高于历史Memory valuations far above history旧周期框架里这是“卖出区”,但 HBM 改写了盈利结构。The old cycle playbook calls this a sell zone, but HBM rewrote the profit structure.
资本纪律史上首次Capital discipline, an industry first存储全行业同步大规模回报股东,金额为上一峰值年的数倍。Memory makers returned capital in sync at a multiple of the prior peak year.
市场的定价规律也在变Even pricing rules are changing资本开支上修从“被罚”变成“云业务加速=豁免”。Capex hikes went from “punished” to “exempt if cloud accelerates”.
投资方法Approach

抓住 AI 机会,有三道难题:看不到、看不懂、买早了Capturing AI opportunities means solving three problems: can't see, can't understand, bought too early

这三道难题,靠多雇几个分析师解决不了——我们用规则加智能体来解。Hiring a few more analysts won't solve them. We solve them with rules plus AI agents.

看不到Can't see

上万家公司,一个人看不过来。Tens of thousands of companies, more than any person can watch.

→ 侦察智能体全市场扫描→ Scout agents sweep the whole market
看不懂Can't understand

新机会,往往在你的认知之外。New opportunities usually sit outside what you already know.

→ 教研智能体三天写出业务 101→ Tutor agents write a business 101 in three days
买早了Bought too early

概念出来就买,结果不一定好。Buying the moment a concept appears often ends badly.

→ 增长引擎六阶段定买卖点→ Six-stage engine framework sets entry & exit

增长引擎六阶段:按阶段定买卖点Six growth-engine stages: entries and exits by stage

S0纯概念Concept
S1产品验证Product validated
S2业绩爆发Results inflect
S3持续兑现Sustained delivery
S4引擎放缓Engine slows
S5行业卷Commoditized
看增长引擎,不看公司Engines, not companies一条引擎是一个需求池,加一种赚钱方式。An engine is one pool of demand plus one way of making money.
买点和卖点,都有清晰规则Clear rules for entries and exits买点有三个:产品验证、收入爆发、利润爆发,在哪个买看贵不贵;卖点:放缓信号连续两个季度成立,或者行业卷起来了。Three entry points: product validation, revenue inflection, profit inflection, chosen by price. Exit when slowdown signals hold for two straight quarters, or the industry commoditizes.
行动跟着证据走Actions follow evidence证据越早,仓位越小,按可能归零来定;每多兑现一个阶段,才多加一档。The earlier the evidence, the smaller the position, sized as if it could go to zero. We add a step only as each stage is confirmed.

人+AI 的投研:人做判断,智能体做覆盖与执行Human + AI research: humans judge, agents cover and execute

人Humans
只做判断,做四件事Judgment only: four things
给直觉Intuition 挑错Find flaws 拍板Decide 判决Adjudicate
AI 智能体AI agents
做覆盖、执行与复核Coverage, execution and review
侦察Scout看得到:全市场扫描财报、产业链与催化剂See it: sweep earnings, value chains and catalysts
教研Tutor看得懂:快速写出新行业、新公司的入门Understand it: fast primers on new industries
研究Research不买早:给每条增长引擎定阶段、定贵贱Not too early: stage every engine, price it
值班Watch跟得住:财报体检、先行指标、多空辩论Keep up: earnings checks, leading indicators, debates
质检QA防犯错:换一家模型独立复核Catch errors: independent review on another model
规则Rules
预注册规则:把人和智能体连起来Pre-registered rules connect humans and agents 人把判断事先写成规则;智能体逐条执行、逐条打分,每个季度回看规则和直觉谁更准。Humans write their judgment down as rules in advance; agents execute and score them line by line, and every quarter we review whether rules or intuition were right.
纪律与风控Discipline & Risk

总仓位怎么定:三步定档,事前预注册How total exposure is set: three steps, pre-registered

每一种走法都提前写好预案:再回撤,按预设档位分批加仓;信号累加,有仓位不踏空。Every path has a playbook written in advance: on renewed drawdowns, add in tranches at preset bands; as signals accumulate, stay positioned.

第一步Step 1

资产层验证打分Asset-level evidence score

核心命题按三组证据季度打分,分数决定基础仓位档——信念有多强,看证据说话。Core theses are scored quarterly on three groups of evidence; the score sets the base position. Conviction must show receipts.

第二步Step 2

周期位置修正Cycle-position adjustment

顶部/底部信号序列+市场状态,在基础档上加减——资产分数相同,周期位置不同,仓位不同。Top/bottom signal sequences and market state adjust the base. Same score, different cycle position, different exposure.

第三步Step 3

情绪哨兵封顶Sentiment-sentinel cap

市场对好消息反应变钝=哨兵亮灯,仓位上限自动压低——防止在别人不买账时独自重仓。When good news stops working, the sentinel lights and the cap drops: no heavy positioning while the market isn't buying.

风险框架Risk framework

多头Long
40–100%
空头Short
0–40%
典型净敞口Typical net
20–80%
总敞口通常Gross, typically
<120%
单一标的大部分Single name, mostly
<10%

极端防御状态下净敞口可降至零附近。方向性策略,非市场中性:净敞口就是表达周期观点的仪表。Net exposure can fall to near zero in extreme defensive regimes. Directional, not market-neutral: net exposure is the dial that expresses our cycle view.

监控体系:定性+定量,逐周读数Monitoring: qualitative + quantitative, read weekly

宏观 · 决定水位Macro · sets the water level
  • 利率路径Rate path
  • 美债收益率:长端与期限溢价UST yields: long end & term premium
  • 主要央行动作Major central banks
  • 关税、出口管制与供应链Tariffs, export controls, supply chain
AI 基本面 · 决定船速(8 项)AI fundamentals · set the boat speed (8 series)
  • 存储合约价环比Memory contract prices
  • 算力租金Compute rents
  • Token 用量增速Token usage growth
  • 现货与长约价差Spot vs contract spread
  • ODM 月营收ODM monthly revenue
  • AI 复合体信用利差AI-complex credit spreads
  • 云厂利润率Cloud margins
  • 云 backlog/RPOCloud backlog / RPO
情绪 · 事件反应记录Sentiment · event-reaction log
  • 市场用真金白银投票,我们逐单记The market votes with real money; we log every vote
  • 同样的消息被卖还是被奖励,决定情绪哨兵是否亮灯Whether the same news is sold or rewarded decides if the sentinel lights
  • 估值和技术是状态,事件反应才是投票Valuation and technicals are states; reactions are votes
透明与安全Transparency & Safety

基金不碰钱,只管钱。安全不靠承诺,靠制度。The fund manages money; it never touches it. Safety by system, not by promise.

资金安全Asset safety
钱在哪Where认购资金直达基金在美国托管银行的独立账户,再以基金机构名义入金券商——不经过任何个人账户。Subscriptions go directly to the fund's segregated account at a US custodian bank, then to the broker in the fund's own name. Never through any personal account.
谁能动Who moves it所有资金划拨须经托管银行与管理公司双重核对;没有任何一方能够单独调用资金。Every transfer requires dual verification by the custodian bank and the management company. No single party can move funds alone.
谁监督Who watchesBVI 金融服务委员会(FSC)监管;独立行政管理人月度净值+季度对账;独立审计师年度审计。Regulated by the BVI Financial Services Commission; monthly NAV and quarterly reconciliation by an independent administrator; annual independent audit.
透明度Transparency
每月Monthly独立核算的净值逐月披露;投资者月信每月一封,市场剧变时加发临时信。Independently calculated NAV every month; a monthly investor letter, plus ad-hoc letters when markets move violently.
每季Quarterly线上复盘说明会,同样的信息同时给到所有投资者。A live review call; the same information reaches every investor at the same time.
每年Annually独立审计。Independent audit.
波动时In turbulence跌得最狠的那个月,我们一个月内与投资者沟通了三次:临时信、说明会、月信。In our toughest month, we spoke to investors three times: an ad-hoc letter, a briefing call and the monthly letter.
基金结构Structure
BVI 专业基金BVI Professional Fund
监管Regulator
BVI FSC
管理人Manager
xResearch Ltd.
独立行政管理人Administrator
NuBright
审计Auditor
Rankin Berkower (Cayman)

所有让人觉得繁琐的合规流程,背后都是对资金安全的保护。Every compliance step that feels tedious is part of what keeps the money safe.

投资者联系Investor Contact

站在周期的拐点前,抓住下一轮 AI 大牛市Stand at the cycle's turning point, and catch the next AI bull market

基金业绩、条款与完整介绍材料,仅向经确认的专业/合格投资者提供。欢迎来信。Performance, terms and full fund materials are available only to verified professional / accredited investors. We welcome your inquiry.