内容来源：https://www.geekwire.com/2025/an-insiders-view-of-the-ai-boom-with-david-shim-of-read-ai/

内容总结：

（本报讯）在近期一场科技论坛中，连续创业者大卫·希姆对比互联网泡沫与当前人工智能热潮时指出，两大关键差异使本轮AI发展更具韧性：成熟的商业模式与用户付费意愿。

这位历经25年技术周期、曾执掌Foursquare并成功将Placed出售给Snap的Read AI首席执行官表示，早期互联网依赖赠品和免费服务吸引用户，而如今企业和消费者正主动为AI工具付费，部分产品上线数月即创收数千万美元。不过他也警示行业存在局部泡沫，例如某些零产品零收入企业获得巨额估值，以及AMD与OpenAI涉及股权激励的芯片采购协议，重现了2000年非典型交易的特征。

"当前确实存在泡沫，但短期内不会破裂，更可能呈现缓慢消解态势。"这位荣获本年度GeekWire大奖的年度CEO预测。其执掌的Read AI已融资超8000万美元，为企业提供跨平台智能会议助手等生产力工具。

在谈及AI技术演进时，希姆提出四大趋势：

1. 专项智能将取代通用助手成为主流，技术成熟后"智能体"概念将隐入基础设施；
2. 人机交互需兼顾心理感受，其公司为AI助手植入应答延迟机制，避免即时响应引发用户疑虑；
3. 全球化部署模式革新，Read AI在哥伦比亚零本地团队获1%人口使用，展现超传统技术的跨境扩张能力；
4. 团队协同AI将释放更大价值，通过整合成员工作数据实现知识共享。

针对最具争议的技术前景，希姆预测"数字孪生"将成新焦点——企业可基于离职员工数据重建其知识库。尽管承认该构想"令人不安"，但他强调这对传承组织记忆具有不可替代的价值。

本次对话源于GeekWire与埃森哲合作的"变革先锋"专题研讨，更多深度内容可通过GeekWire播客频道获取。

中文翻译：

[编者按：《变革先锋》是GeekWire推出的独立专题报道及2026年系列活动，由埃森哲赞助支持，深入探讨AI智能体崛起背后的人物、企业与思想内核。]

互联网泡沫与当今AI热潮有何本质区别？在连续创业者大卫·希姆看来，早期互联网规模性缺失的两大要素正是：切实的商业模式与愿意付费的用户。

早期互联网的使用体验建立在免费模式之上，依赖礼品卡、免运费等补贴手段。而如今，企业和消费者正真金白银地为AI工具买单——这些工具在数月内就能创造数千万美元营收，用户确实从中看到了实际价值。

作为Read AI联合创始人兼CEO，希姆在过去25年历经多个技术周期并成功创立多家企业。他并未全盘否定AI泡沫的存在，而是指出行业边缘存在的投机现象：某些既无产品又无营收的公司竟获得巨额估值，这种状况被他形容为"百分之百的泡沫迹象"。

他同时以AMD与OpenAI的合作为例说明市场泡沫——这家芯片制造商通过股权激励捆绑大额芯片采购协议。这种带有以物易物色彩的特殊金融操作，隐约带着2000年交易热潮的气息，曾短暂推高AMD股价。

但希姆认为，这类案例更应视为特例，而非系统性风险警示。

"确实存在泡沫，但短期内不会破裂。"希姆表示，"最终更可能呈现缓慢消胀的态势。"

这位刚荣获本年度GeekWire大奖年度CEO的企业家，曾执掌Foursquare并将初创公司Placed出售给Snap。如今他领导的Read AI已融资超8000万美元，其跨平台AI会议助手与效率工具已获得多家大型企业客户。

上述观点出自他与GeekWire联合创始人约翰·库克的深度对谈。此次交流发生在埃森哲与GeekWire联合举办的晚宴上，双方就人工智能、工作效率与未来职场等议题展开讨论，这也是GeekWire新推专栏"变革先锋"的系列对话之一。

本期GeekWire播客完整呈现这场精彩对话，欢迎在苹果播客、Spotify等平台订阅收听。以下为对话精要：

成功AI智能体专注解决特定问题：最有效的AI应用将成为专注特定任务的隐形基础设施，而非面面俱到的通用助手。随着技术成熟与深度整合，"智能体"这一称谓本身将逐渐隐入幕后。

人类心理影响AI部署策略：ReadAI内部正在测试名为"Ada"的智能助手，可通过学习用户沟通模式自动安排会议。其响应速度极快，公司特意在回复中设置延迟——因为即时回复会让人产生"消息未被仔细阅读"的不安感。

全球化推广无需传统本地化：ReadAI在哥伦比亚零人员配置的情况下触达全国1%人口，这印证了AI具备传统技术难以企及的全球化扩张能力。

"多用户AI"将释放更大价值：希姆指出，仅掌握单人数据的AI价值有限。关键在于实现团队级AI互联，通过调取同事工作数据（包括未参与的会议记录和未阅览文件）来解答问题。

"数字分身"引发伦理争议：希姆预测未来企业可基于离职员工的工作数据"复活"其数字分身，用以查询仅该员工掌握的机构知识。尽管这个设想听起来颇具争议且"令人不安"，但对解决特定历史问题具有无可替代的价值。

欢迎在苹果播客、Spotify等平台订阅GeekWire，获取更多科技前沿资讯。

英文来源：

[Editor’s Note: Agents of Transformation is an independent GeekWire series and 2026 event, underwritten by Accenture, exploring the people, companies, and ideas behind the rise of AI agents.]
What separates the dot-com bubble from today’s AI boom? For serial entrepreneur David Shim, it’s two things the early internet never had at scale: real business models and customers willing to pay.
People used the early internet because it was free and subsidized by incentives like gift certificates and free shipping. Today, he said, companies and consumers are paying real money and finding actual value in AI tools that are scaling to tens of millions in revenue within months.
But the Read AI co-founder and CEO, who has built and led companies through multiple tech cycles over the past 25 years, doesn’t dismiss the notion of an AI bubble entirely. Shim pointed to the speculative “edges” of the industry, where some companies are securing massive valuations despite having no product and no revenue — a phenomenon he described as “100% bubbly.”
He also cited AMD’s deal with OpenAI — in which the chipmaker offered stock incentives tied to a large chip purchase — as another example of froth at the margins. The arrangement had “a little bit” of a 2000-era feel of trading, bartering and unusual financial engineering that briefly boosted AMD’s stock.
But even that, in his view, is more of an outlier than a systemic warning sign.
“I think it’s a bubble, but I don’t think it’s going to burst anytime soon,” Shim said. “And so I think it’s going to be more of a slow release at the end of the day.”
Shim, who was named CEO of the Year at this year’s GeekWire Awards, previously led Foursquare and sold the startup Placed to Snap. He now leads Read AI, which has raised more than $80 million and landed major enterprise customers for its cross-platform AI meeting assistant and productivity tools.
He made the comments during a wide-ranging interview with GeekWire co-founder John Cook. They spoke about AI, productivity, and the future of work at a recent dinner event hosted in partnership with Accenture, in conjunction with GeekWire’s new “Agents of Transformation” editorial series.
We’re featuring the discussion on this episode of the GeekWire Podcast. Listen above, and subscribe to GeekWire in Apple Podcasts, Spotify, or wherever you listen. Continue reading for more takeaways.
Successful AI agents solve specific problems: The most effective AI implementations will be invisible infrastructure focused on particular tasks, not broad all-purpose assistants. The term “agents” itself will fade into the background as the technology matures and becomes more integrated.
Human psychology is shaping AI deployment: Internally, ReadAI is testing an AI assistant named “Ada” that schedules meetings by learning users’ communication patterns and priorities. It works so quickly, he said, that Read AI is building delays into its responses, after finding that quick replies “freak people out,” making them think their messages didn’t get a careful read.
Global adoption is happening without traditional localization: Read AI captured 1% of Colombia’s population without local staff or employees, demonstrating AI’s ability to scale internationally in ways previous technologies couldn’t.
“Multiplayer AI” will unlock more value: Shim says an AI’s value is limited when it only knows one person’s data. He believes one key is connecting AI across entire teams, to answer questions by pulling information from a colleague’s work, including meetings you didn’t attend and files you’ve never seen.
“Digital Twins” are the next, controversial frontier: Shim predicts a future in which a departed employee can be “resurrected” from their work data, allowing companies to query that person’s institutional knowledge. The idea sounds controversial and “a little bit scary,” he said, but it could be invaluable for answering questions that only the former employee would have known.
Subscribe to GeekWire in Apple Podcasts, Spotify, or wherever you listen.