以下是翻译后的内容： 这期 Odd Lots 播客节目采访了 Lux Capital 的联合创始人兼管理合伙人 Josh Wolf，讨论了人工智能的现状与未来、风险投资的激励机制以及新兴趋势。 Wolf 在多个方面挑战了普遍的共识，首先是 Alphabet (Google)，他认为这家公司是人工智能领域的“黑马”，被低估了。他指出 Gemini 和该公司庞大的 YouTube 数据存储库是重要的优势。他还认为苹果公司也可能成为竞争者。 他认为，虽然人工智能对日常生活的影响被低估了，但许多人工智能公司的估值却被过度炒作了。他认为，人工智能对知识工作者，特别是入门级职位的劳动破坏，将比预期的更大。他挑战了关于 GPU 无尽需求的普遍观点，指出苹果的研究表明，大型语言模型可以在使用闪存的设备上运行，这可能会将计算负担转移到边缘推理，从而影响 GPU 的需求。 对话转移到 “人才收购 (aquahire)” 的问题，即大型科技公司收购较小的人工智能公司，主要目的是为了获得它们的人才。Wolf 承认这是一个严重的问题，破坏了传统的风险投资模式。司法部和联邦贸易委员会的压制阻碍了并购活动，迫使大型科技公司通过授权和人才收购的方式来获得人才，而不是进行并购。他预测，风险投资交易将出现钟摆效应，转向对投资者更有利的条款，以防止这种情况发生。他强调了投资者和创始人之间“社会契约”的重要性，认为人才收购破坏了这种信任。 Joe Weisenthal 和 Wolf 探讨了风险投资生态系统中固有的委托代理问题，强调了有限合伙人 (LPs)、风险投资家 (VCs) 和创始人之间可能存在的不一致之处。Wolf 概述了三个层次的激励机制：有限合伙人寻求回报，风险投资家通过跑赢大盘来竞争资本，以及创始人在流动性事件中寻找出路。他预测风险投资行业将出现一次洗牌，由于合伙关系问题和储备金不足，小型基金（“minos”）的“灭绝率”将很高。他将这些与大型公司（“megas”）进行对比，后者正在向资产收集转型，并专注于为创始人创造世代财富。他承认，风险投资家可能会为了吸引有限合伙人的资金而吹嘘对热门交易的投资，这可能会凌驾于对高回报的追求之上。 Wolf 深入探讨了创始人流动性的复杂性，承认了在激励一致和允许创始人早期降低风险之间的紧张关系。他分享了一个个人轶事，讲述了他错过了一次为创始人提供早期流动性的机会，这可能改变了公司出售的结果。 关于开源人工智能模型，Wolf 反驳了闭源模型本质上更有价值的假设。他以 Hugging Face 为例，说明了一个成功的开源平台，并认为长期价值将归于拥有大型、孤立的专有数据集的实体，如彭博、Meta 和制药公司，而不是那些拥有闭源基础模型的实体。 谈到新兴趋势和潜在的过度炒作领域，Wolf 认为二维人工智能（语音、视频、图像、文本）正接近“足够好”的程度。他对三维人工智能（机器人）和生物学（药物发现）表现出更大的热情，强调了训练数据的稀缺性和这些领域的复杂性。他设想了一个由“生活记录”设备（被动录音设备）主导的未来，并预测随着人类与人工智能模型建立更深层次的关系，“人工智能权利”倡导将兴起。

This Odd Lots podcast episode features a conversation with Josh Wolf, co-founder and managing partner at Lux Capital, discussing the current state and future of AI, venture capital incentives, and emerging trends. Wolf challenges the consensus view on several fronts, starting with Alphabet (Google), which he believes is a "dark horse" and underestimated player in the AI space. He points to Gemini and the company's vast repository of YouTube data as significant advantages. He also suggests Apple is a contender. He argues that while AI's impact on daily life is underhyped, the valuations of many AI companies are overhyped. He believes the labor destruction in knowledge worker jobs, particularly entry-level positions, will be greater than anticipated. He challenges the prevailing narrative about the endless demand for GPUs, pointing to Apple's research suggesting large language models can run on devices using flash memory, potentially shifting the compute burden to edge inference and impacting GPU demand. The conversation shifts to the issue of "aquahires," where larger tech companies acquire smaller AI firms primarily for their talent. Wolf acknowledges this is a serious problem that undermines the traditional venture capital model. The suppressive DOJ and FTC, hindering M&A activity, are forcing big tech companies to license and aquahire talent rather than engage in mergers and acquisitions. He predicts a pendulum swing towards more investor-friendly terms in venture capital deals to protect against this. He emphasizes the importance of a "social contract" between investors and founders, arguing that aquahires break this trust. Joe Weisenthal and Wolf explore the principal-agent problems inherent in the VC ecosystem, highlighting potential misalignments between LPs, VCs, and founders. Wolf outlines three layers of incentives: LPs seeking returns, VCs competing for capital by outperforming, and founders navigating liquidity events. He predicts a shakeout in the VC industry, with a high "extinction rate" among smaller funds ("minos") due to partnership struggles and inadequate reserves. He contrasts these with larger firms ("megas") transitioning to asset-gathering and focusing on generational wealth for founders. He acknowledges the temptation for VCs to tout investments in hot deals to attract LP money, potentially overriding the pursuit of high returns. Wolf delves into the complexities of founder liquidity, acknowledging the tension between aligning incentives and allowing founders to de-risk early. He shares a personal anecdote about missing an opportunity to provide early liquidity to a founder, potentially altering the outcome of a company sale. Regarding open-source AI models, Wolf counters the assumption that closed-source models are inherently more valuable. He cites Hugging Face as an example of a successful open-source platform and argues that the long-term value will accrue to entities with large, siloed proprietary data sets like Bloomberg, Meta, and pharma companies, rather than those owning closed foundation models. Turning to emerging trends and potential overhyped areas, Wolf suggests that two-dimensional AI (voice, video, image, text) is reaching a point of "good enough." He expresses greater enthusiasm for three-dimensional AI (robotics) and biology (drug discovery), emphasizing the scarcity of training data and the complexity of these domains. He envisions a future dominated by "life-cording" devices—passive recording devices—and predicts a rise in "AI rights" advocacy as humans develop deeper relationships with AI models.