Suno Reaches $300 Million Revenue as AI Music Shifts Users from Listeners to Creators

Suno营收达3亿美元，AI音乐推动用户从听众转变为创作者 Suno预计，生成式AI将通过优先考虑结构化叙事与艺术家和粉丝的直接互动（而非单纯追求原始音频保真度），推动音乐从被动流媒体收听转向主动创作。 Suno创始人兼CEO Mikey Shulman与主持人Sonya Huang展开对谈，探讨了为何Suno有90%的日活跃用户在主动创作音乐而非被动收听。他详细阐述了从第一性原理对原始声波进行建模的技术理念，以及为何选择自回归而非扩散模型以优先生成结构完整、具备叙事性的歌曲。Shulman还解释了音乐AI是如何立足于人类偏好而非单纯依靠算力规模扩展，并分享了包括与华纳音乐集团达成里程碑式和解在内的关键合作，旨在打造全新的互动式粉丝体验。 Suno 彻底颠覆了传统音乐格局，平台 90% 的日活用户都在主动创作而非被动收听，让创作行为本身成为一种旨在获取乐趣与成就感的“创意娱乐”。 (0:00) 在底层技术上，Suno 摒弃了硬编码西方 12 音阶乐理的做法，从第一性原理直接对 48 kHz 的 float32 原始声波建模，从而激发了微音程与跨风格流派融合等涌现能力。 (5:00) 创始人 Mikey Shulman 强调音乐 AI 本质是“人类主观偏好问题”而非单纯的算力扩展问题，并在模型路线上坚持选择自回归而非扩散模型，优先保障多分钟完整歌曲的叙事与结构完整性。 (10:00) Suno 的年化营收运行率已达 3 亿美元，并通过与华纳音乐（Warner Music Group）达成标志性合作，致力于重塑 25 年来固步自封的数字化流媒体体验，探索现场音乐实时共创等全新形态。 (22:20)

Suno co-founder and CEO Mikey Shulman argues that generative AI is transforming music from passive consumption into active, creative entertainment driven by human preference rather than raw compute scale. Active Creation as Primary Entertainment (0:00) Before Suno, global music consumption was split between a small group of creators and billions of passive listeners. Shulman notes that around 90% of Suno's daily users actively create music rather than simply listen. Instead of seeking commercial distribution or monetization, users largely engage with the tool for personal fulfillment and the emotional reward of the creative process itself, comparable to home cooking or programming side projects. While critics express concern over low-quality AI "slop," Shulman compares current developments to earlier disruptions like bedroom laptop production, which expanded both low-quality tracks and breakout hits. He maintains that human guidance remains essential to compelling music, citing creators like poet Zania Monet, who set a decade of poetry to music, as well as established artists using AI generated segments inside wider production pipelines. First-Principles Modeling Over Scale Benchmarks (5:00) Suno models music from first principles as raw audio waveforms—specifically 32-bit floats sampled at 48 kHz—rather than constraining generation within traditional 12-tone Western music theory or set instrument banks. This architectural decision enables emergent combinations, such as trap featuring sitars, country over 808 sub-bass, and microtonal compositions. Prompt handling pairs large language models for lyrical expansion with audio models processing acoustic descriptions into unified sound. Unlike large language models that benefit predictably from increased compute parameters on static benchmarks, Shulman views music AI as a preference optimization challenge. Because music lacks objective correct answers, internal preference metrics and reinforcement learning based on subjective human taste determine progress. Suno keeps models comparatively compact to preserve low latency, enabling interactive and fluid creation. Contrarian Product Architecture and Distribution (25:35) Because Shulman does not view raw models as defensible moats against well-funded hyperscalers, Suno concentrates on interface design and specialized consumer product decisions. After starting as a Discord bot, Suno shifted to a dedicated web client in late 2023, seeing 90% of user traffic migrate to the browser within five days. Technically, Suno prioritized full-length vocal songs over background instrumentals, betting that lyrical narrative drives listener connection. The team also chose autoregressive modeling over diffusion. Although diffusion yielded crisper short clips, autoregression enabled coherent 3-to-3.5-minute narrative arcs, which Suno considered more critical to human engagement than initial transient fidelity. Industry Partnerships and Social Roadmap (22:20) Suno reached a $300 million revenue run rate while pursuing direct integration with traditional industry incumbents. Rejecting the idea that record labels are obsolete, the company formed a settlement and partnership with Warner Music Group to develop interactive products connecting fans and artists, aiming to break out of a passive streaming model that Shulman argues has remained largely unchanged for 25 years. Looking forward, Suno is testing music video generation and designing features for asynchronous co-creation using stems, templates, and custom voice models. Shulman envisions interactive music at live venues within 12 months, including co-creation between festival audiences and headliners at events such as Coachella. Shulman maintains that as routine labor automates, human demand for active, joyful creation will expand. However, commercial adoption still faces the challenge of building novel consumer behaviors around interactive media rather than relying on established streaming formats.

Suno联合创始人兼首席执行官Mikey Shulman指出，音乐AI正在打破长达25年未变的被动流媒体消费范式，将其转向以自我表达与心理满足为核心的“创意娱乐”；他主张音乐AI的核心挑战在于对齐人类主观审美偏好而非单凭算力扩展，并通过第一性原理对连续物理波形建模以释放全新音乐形态。 范式重塑：从被动消费到创意娱乐 (0:00) Shulman指出，全球传统音乐行业长期处于极少数人创作、数十亿人被动消费的割裂状态，数字音乐体验在过去25年中基本局限于流媒体播放。Suno改变了这一格局，其每日活跃用户中有90%在主动进行内容创作。Shulman强调，多数用户生成音乐并非为了导出或商业分发，而是享受创作过程本身带来的纯粹乐趣与成就感。他将这种模式定义为“创意娱乐”（creative entertainment），类比于大众即便能在餐馆吃到更精美的菜肴仍会享受家庭烹饪的乐趣，抑或是开发者使用编码工具自主构建项目的快感。 针对AI生成内容被贬称为“垃圾信息”（slop）的行业争论，Shulman认为该词汇缺乏清晰定义。他以个人与5岁孩子共同创作歌曲为例，说明私人创作即便对外界毫无价值，对创作者本人却意义重大。他回顾了笔记本电脑普及催生卧室制作人的历史，指出生产门槛降低虽会带来海量低质音乐，但同时也会大幅抬升优质作品的数量上限，并诞生全新流派与明星。例如，诗人Zania Monet通过Suno将其积累十年的诗歌制作成歌，赢得了唱片合约与广泛受众；大量专业音乐人也已将Suno作为模块化工具嵌入其既有制作管线中。 纯波形建模与自回归长音频架构 (5:00) 拥有哈佛量子计算物理博士背景的Shulman从第一性原理出发构建Suno系统，坚决摒弃了将西方十二平均律或200种传统乐器分类硬编码进模型架构的传统思路。他认为这些乐理预设会永久性地锁死模型的生成广度。Suno将声音直接建模为在48 kHz采样率下、以32位浮点数（float32）表示的连续原始音频波形。系统将用户输入的提示词分为两组流水线处理：大语言模型负责扩展歌词与风格描述，而底层音频模型则将人声与乐器作为统一的纯粹声音端到端生成，这使得微音程（microtonal）作品以及西塔琴陷阱乐、808低音乡村乐等跨流派融合自然涌现。 在模型选型与产品策略上，Suno做出了多项反共识抉择。在早期多数竞品依赖扩散模型生成10至12秒高清晰度短片段的背景下，Suno坚持选用自回归（autoregression）架构。Shulman解释，团队选择牺牲部分早期的瞬态音频清晰度，优先确保系统从一开始就能生成长达3至3.5分钟、具备完整结构起伏与叙事张力的整首歌曲。此外，Suno押注于包含人声歌词的叙事性歌曲而非低门槛的背景音乐，因为具有人声辨识度的故事更容易引发人类深层的情感共鸣。 核心逻辑：偏好对齐而非算力扩展 (10:00) Shulman明确断言，与大语言模型通过扩大算力和参数量可在既定基准上取得可预测提升不同，音乐AI本质上“不是一个算力扩展问题”（not a scale problem）。音乐领域不存在客观的标准答案或固定的衡量基准，技术突破的关键完全取决于模型与人类主观审美偏好之间的对齐程度。为了保证流畅敏捷的交互体验，Suno有意将模型体积保持在紧凑范围以确保高速生成，并通过强化学习（RL）利用人类偏好数据训练模型的结构连贯性与情绪感染力，且这一过程无需面对文本模型特有的阿谀奉承（sycophancy）风险。 尽管Suno V5版本的发布实现了音频保真度的飞跃，但Shulman强调创意模型的演进具有高度非线性。团队内部标准测试中10%到15%的模型胜率提升，并不一定能直接转化为用户的实际采纳或情感投入。因此，Suno在持续推进V6和V7等研发路线的同时，采取高频稳健的产品发布节奏，避免为了追求单一的理论化终极模型而长期推迟版本迭代。 商业壁垒、唱片工业合作与社交愿景 (22:20) 在商业化与产品壁垒层面，Suno披露其年化收入运行率（revenue run rate）已达到3亿美元。Shulman坦承，单纯的AI模型无法构成创业公司的长久护城河，因为大型科技巨头在算力投资上具有不可比拟的资金优势，因此Suno将精力聚焦于UI、UX和产品化落地。数据显示，在2023年末团队推出简易网页端后，仅耗时5天便有90%的用户活动从原有的Discord频道快速迁移至独立网页平台。 在产业链协作方面，Suno驳斥了技术公司颠覆唱片巨头的零和叙事，强调唱片公司在孵化明星与文化引领上具备核心价值。Suno与华纳音乐集团（Warner Music Group）达成了标志性合作协议与诉讼和解，共同开发旨在加深歌迷与艺人连接的正向互动产品。展望未来12个月，Shulman透露团队正在公测内置音乐视频生成器，并计划推进同步/异步社交模板、未发行分轨（stems）重混以及自定义人声克隆功能；他还预测，类似在科切拉（Coachella）音乐节上由成千上万名观众与主舞台艺人共同即时共创单曲的互动体验，将在一年内成为现实。 Shulman描绘了AI将数十亿被动受众转变为积极创作者的技术愿景，并展示了3亿美元运行率下的产品势能与唱片业合作进展；然而，去中心化的大众生成内容能否真正沉淀为持久的流行文化经典，以及现场万人共创与商业化合作模式能否在复杂的版权与利益分配框架中稳定落地，仍有待更长时间维度的市场检验。

## Background 本期节目由红杉资本（Sequoia Capital）合伙人 Sonya Huang 主持并在其播客渠道发布于 2026 年 5 月 13 日。节目深入探讨了 AI 音乐平台 Suno 如何颠覆传统音乐消费模式，详细拆解了 Suno 将“创作本身作为娱乐”的核心定位、完全抛弃乐理假设的底层技术豪赌，以及与华纳音乐合作重塑长达 25 年未变的数字音乐体验的行业愿景。 ## TL;DR Suno 创始人兼 CEO Mikey Shulman 阐述了平台 90% 日活用户都在主动造歌的颠覆性现象，指出生成式 AI 正在将音乐从被动消费转变为以快乐和自我实现为核心的“创作型娱乐”。在技术路线上，Suno 彻底抛弃西方乐理硬编码，将音乐还原为 48kHz 的连续声波建模，并逆向押注自回归架构以生成包含起承转合与叙事人声的完整歌曲，同时证明了音乐生成依赖人类主观审美与极低延迟体验，而非盲目堆叠模型参数的 Scaling Law。面对所谓“AI 垃圾（Slop）”的质疑，Shulman 强调个人表达的情感价值远超冰冷的播放量指标，并分享了 Suno 与华纳音乐合作探索共创互动、推动万人演唱会实时交互及拓展 MV 视频叙事的全新前沿。 ## Atmosphere & style 整场对话洋溢着跨界探索的极客激情与真诚温润的人文情怀。身兼量子计算物理学博士与资深乐手的 Mikey Shulman 举重若轻，既有对技术第一性原理的严谨反思，又带着与 5 岁孩子写歌时的纯粹喜悦，与同样拥有古典音乐背景的投资人 Sonya Huang 碰撞出极高频的共鸣。两人从早期粗糙 Discord 原型的废寝忘食，聊到对抗大厂围剿的清醒与对未来机器人时代人类闲暇价值的终极追问，语调轻松幽默却格局宏大，让听众深刻感受到消费级 AI 背后那股罕见的、因创造本身而生的蓬勃生命力。 ## Bulletin - `00:00` Suno 平台展现出与传统流媒体截然相反的用户行为，90% 的日活用户会亲自动手创作音乐，公司年化运行营收（ARR）已突破 3 亿美元，确立了“创作过程即娱乐”的消费级 AI 新范式。 - `05:00` 技术路线上 Suno 彻底摒弃传统 12 半音等乐理约束，端到端直接对 48kHz 原始声波连续建模，并逆向押注自回归架构以生成带人声叙事的 3 分钟完整歌曲，而非采用行业主流的扩散模型生成高清短切片。 - `11:15` CEO Mikey Shulman 指出音乐生成并非 LLM 式的 Scaling Law 问题，由于缺乏客观基准测试，Suno 选用轻量化架构保障极低推理延迟，并依赖庞大用户群产生的真实审美偏好数据飞轮驱动强化学习。 - `22:15` Suno 与华纳音乐达成里程碑式合作，旨在打破数字音乐 25 年来仅作为静态音频流的停滞格局，开辟粉丝与艺人共创互动模式，并计划在 12 个月内探索音乐节主舞台上的万人实时交互共创演唱会。 ## Key takeaways - `15:45` 颠覆 80 亿人的被动消费：Suno 上 90% 的日活用户都在主动创作，且多数并不为了外部发行变现，创作本身带来的多巴胺与充实感即是娱乐，与家庭下厨或使用 Claude Code 编写个人工具类似。 - `05:00` 基于声波第一性原理建模：系统不预设人声、乐器或传统 12 半音音阶规则，将音频视作每秒 48,000 次采样的连续物理波形（Float32），赋予模型无上限的表达自由与跨流派（如 Trap 融入西塔琴）涌现能力。 - `11:15` 音乐生成并非 Scaling Law 问题：音乐涉及高度主观的人类审美，没有可供刷榜的客观 Benchmark，盲目堆叠参数与算力的边际效用极低；采用紧凑轻量模型以实现极低延迟交互才是产品体验的核心。 - `11:15` 海量偏好数据构建算法壁垒：庞大的活跃创作生态沉淀了真实的人类偏好对比数据，不仅使强化学习免受文本大模型常见的“阿谀奉承（Sycophancy）”困扰，更是团队研发底层音乐生成前沿算法的前提条件。 - `13:45` 坚决反对做“AI 版 Spotify”：数字音乐过去 30 年逐渐沦为廉价背景音，但音乐在人类进化中承载着核心社交与情感纽带；AI 音乐的核心使命是放大音乐的社交互动与参与感，而非做冷冰冰的自动化灌水工具。 - `18:00` 重新定义“AI 垃圾（Slop）”的价值标准：衡量音乐生成的标准不应只有流媒体播放量与大众商业指标；哪怕五岁孩子与父母共同创作的歌曲在冷酷数据看来是废案，但只要对创作者个人具有真实情感意义就绝非垃圾。 - `20:00` 重演卧室制作人浪潮与专业融合：AI 平权让数十亿人具备写歌能力，虽然会带来平庸内容的激增，但同样会孵化出全新视角的超级巨星（如诗人转型的 Zania Monet），且 Suno 片段已被广泛融入主流专业音乐制作流程中。 - `22:15` 联手华纳音乐重构 25 年未变的数字体验：自 Napster、iTunes 到 Spotify，数字音乐消费在四分之一个世纪里停滞于单向静态播放；与唱片公司正和合作旨在构建偶像与粉丝隔空协同创作的全新互动变现形式。 - `24:45` 万人同频共创的“宗教式”现场构想：设想未来在科切拉等音乐节主舞台上举办双向交互式演唱会，台上音乐人与成千上万名台下观众实时协同写歌并合唱，重现人类数千年来大规模群体吟唱的崇高情感共振。 - `26:30` 消费级产品的护城河在体验而非模型：消费端用户对产品粗糙度容忍度极低，且面对拥有无限算力与资本的科技巨头（如 Google），单纯靠模型底座无法建立长期壁垒，Suno 将组织定位于“取悦普通人的音乐公司”。 - `28:30` 两项关键逆向产品豪赌：早期竞品普遍用扩散模型追求 10-12 秒的高清纯音乐切片，Suno 逆势押注自回归架构强行生成带人声叙事的 3 分钟完整歌曲，宁愿忍受初期音质噪点也要优先保障故事完整性，最终跑通 3 亿美元 ARR。 - `30:00` 未来 12 个月重心聚焦社交共创与真声印记：推进基于音轨模板的即兴接力（Riffing）与跨时空协同创作，并上线用户原声演唱功能，利用人耳对熟悉嗓音的高度敏感性建立强烈的心理认同与社交共鸣。 - `31:30` 重振 MV 叙事艺术，让音乐重回前台注意力：反对将音乐贬为网络视频底部的低质附属背景音（BGM），正在内测视频生成产品（Video Product Beta），通过紧凑的视听融合叙事将用户重新拽回音乐沉浸体验的核心。 - `32:35` 消费级 AI 创业的终极命题在于创造“未知”：相比企业端对存量流程显而易见的自动化降本增效，消费级 AI 需要想象力去开辟前所未有的玩乐方式；当未来算法接管所有繁重工作时，创造性娱乐将成为人类抵抗空虚的充实寄托。 ## Detailed walkthrough ### `00:00` 创作即娱乐：颠覆传统音乐消费模式 在 Suno 问世之前，几乎所有人对于音乐都仅仅是“消费者”。放眼全球 80 亿人口，真正有能力创作音乐的人凤毛麟角，绝大多数人都只是被动收听。然而 Suno 带来了一个颠覆性的现象：在任何给定的日子里，平台上有 90% 的日活跃用户都在主动创作内容。更不可思议的是，用户创作音乐并不是为了像传统音乐人那样把作品发布到别处发行或变现，他们纯粹是为了享受创作过程本身带来的乐趣、愉悦感与自我实现。换句话说，**创作本身就是娱乐**。红杉资本合伙人 Sonya Huang 指出，这代表着消费级 AI 应用中最为新颖的范式转变，将 Suno 从单一的音乐制作工具升格为极具互动性的创意娱乐平台。 谈及跨界背景，Suno 联合创始人兼 CEO Mikey Shulman 拥有极为非典型的经历：他曾获得哈佛大学物理学博士学位，专注于固态自旋量子计算研究，如今却掌舵全球规模最大的 AI 音乐平台。Shulman 调侃自己“在纸面上看根本没有资格做消费娱乐公司”。但正如 30 年前大批物理学家涌向华尔街从事量化交易一样，物理学向 AI 迁移已屡见不鲜。他谦虚地表示自己只是个普通的物理学者，联合创始人中甚至有比他优秀的物理学家。这段经历给他最大的启示是：**在两个看似毫无关联的领域交汇处探索，往往蕴藏着巨大的历史机遇**——无论是音乐与科技的结合，还是量子力学与低温微波工程的碰撞，交叉点上总有突破的可能。 ### `02:40` 从 Discord 雏形到突破：推翻计算不可能的“草稿纸估算” Suno 诞生的前奏充满了误打误撞与认知反转。在 ChatGPT 爆发之前，团队深爱音频技术，但当时的粗略估算（back-of-the-envelope math）显示：要生成真正高质感、完整的音乐，在算力需求、模型参数量和表达能力上，还差着“几个数量级”。因为声音本质上是连续且极为复杂的物理波形，不像文本那样由离散的词元（tokens）构成。因此，团队最初成立公司时，只想用相关技术去“理解和解析音频”，而非“生成音乐”。 然而在早期研发中，团队在音频压缩等核心技术上取得了出人意料的突破。Shulman 笑称“数学本身没算错，只是我们找到了用极高效率压缩音频的方法”，这让原本以为遥不可及的算力门槛断崖式下降。最初生成的音乐虽然粗糙不堪——甚至在上线 Discord 之前，模型只能生成大约 12.5 秒的片段，还经常完全无视用户输入的歌词——但团队依然为之废寝忘食，玩得不亦乐乎。看到 Midjourney 借助 Discord 社区病毒式传播的路径，他们也顺势上线了一个 Discord Bot。当时 LangChain 创始人 Harrison Chase 作为前十名最早期的核心用户之一，在频道里沉迷于连续做歌。Shulman 感慨，很多人曾劝他们别做音乐，因为音乐是公认最难啃的商业硬骨头，而做语音（Speech）技术商业场景更广、更赚钱；但当团队在深夜通宵把玩一个产品并且兴奋得根本不想睡觉时，那就是最明确的信号：这就是你命中注定该做的事。 - **早期的技术局限**：在 Discord 之前，每次只能生成约 12.5 秒的音频切片，且模型经常无法准确执行让其演唱指定歌词的指令。 - **Kensho 系的基因传承**：Shulman 与 Harrison Chase 等人均出自金融科技公司 Kensho。Shulman 盛赞 Kensho 创始人 Daniel Nadler 建立了极高的“人才密度”（Talent Density），善于招募极具潜力的非传统背景年轻人；当年团队就常在合伙人的地下室举行即兴演奏会（Jam Sessions），也为日后的创业埋下了文化火种。Shulman 本人也是资深乐手，从小弹钢琴，12 岁起开始弹贝斯。 ```mermaid flowchart TD accTitle: Suno 早期演进历程 accDescr: 展示 Suno 如何从最初的技术怀疑转向最终验证产品价值的完整演进链条 subgraph Phase1["技术探索阶段"] A["最初推论：音乐生成不可行<br/>（音质成熟需差几个数量级的算力）"] B["转向音频理解与解析技术"] C["取得高效音频压缩技术突破"] A --> B --> C end subgraph Phase2["原型验证阶段"] D["12.5 秒粗糙片段，偶尔漏词"] E["创始团队废寝忘食，深度沉迷"] C --> D --> E end subgraph Phase3["上线与扩散阶段"] F["效仿 Midjourney 推出 Discord 机器人"] G["早期死忠用户（如 Harrison Chase）涌现"] H["坚定聚焦音乐领域，推翻做语音的建议"] E --> F --> G --> H end ``` ### `05:00` 抛弃乐理假设：把一切回归为 48kHz 的连续声波 当用户在 Suno 中输入类似“一段关于公路旅行的 90 年代欢快嘻哈音乐（upbeat 90s hip hop track about a road trip）”的 Prompt 时，底层系统的运转逻辑展现了他们特立独行的架构哲学。首先，系统借助大语言模型（LLM）将抽象意图转化为具体歌词（分析“公路旅行”可能包含的情境与叙事，即便信息不全也没关系，用户可以随时迭代），并将“90 年代嘻哈”拆解拓展为模型能理解的流派、风格、乐器配器等线索。随后，音乐模型将歌词与风格控制信号融为一体，直接输出完整的声音。 在这个流程中，最核心也是最艰难的技术决策在于：**团队彻底抛弃了所有音乐理论的先验知识**。模型根本不知道什么是“人声”、什么是“乐器”，也不知道具体存在哪种乐器。 ```mermaid flowchart LR accTitle: Suno 端到端音乐生成架构 accDescr: 展示从用户自然语言输入到最终 48kHz 原始声波渲染的端到端数据流 subgraph Input["用户输入与拓展"] P["Prompt: 90s 公路旅行嘻哈"] LLM["大语言模型解析"] P --> LLM end subgraph Prompts["信号分解"] L["歌词生成与迭代"] S["风格与流派细化线索"] LLM --> L LLM --> S end subgraph AudioEngine["Suno 核心音频模型"] Core["端到端声波连续建模<br/>（无乐理限制 / 无固定音符）"] L --> Core S --> Core end subgraph Output["最终声学呈现"] WAV["48,000 Hz / Float32 原始声波"] Core --> WAV end ``` Shulman 强调，在西方传统乐理中，一个八度内只有 12 个半音（12 tones）；如果在模型中把这 12 个音符作为硬编码约束，模型就永远只能发出这 12 个音；如果告诉模型世界上只有 200 种乐器，生成的作品就永远被束缚在这 200 种音色之中，“那么未来就不可能通过 Suno 诞生下一个 Skrillex”。 因此，Suno 从第一天起就选择从第一性原理出发，把音乐当成没有任何人为规则的纯粹物理声音： - **本质抽象**：音乐不过是一道以每秒 48,000 次采样的连续声波（48kHz Sample Rate），每一个采样点就是一个连续的 Float32 浮点数。 - **表达自由**：完全消除规则约束后，生成的上限只取决于用户的想象力和用语言描述的能力。 ### `07:30` 涌现的全新形态：从跨流派乱炖到微音程探索 完全基于声波第一性原理训练模型后，系统并未单纯停留在对现有音乐风格的简单复刻上，而是展现出了惊人的新特性涌现（Emergence）。最常见的就是毫无道理却又浑然天成的跨流派融合：用户频繁创造出传统制作人很难想到的搭配，例如在 Trap 节奏中融入印度西塔琴（Sitar），或者在乡村音乐（Country）的骨架里植入沉重的 808 鼓点。更前沿的用户甚至开始用它生成打破西方传统平均律的“微音程音乐（Microtonal Music）”。 在谈到模型对不同流派的擅长程度时，Shulman 给出了解释： - **擅长且下限高**：对于流行乐（Pop）和乡村乐（Country），Suno 表现得极其出色。这在很大程度上是因为这类商业风格本身具有较强的范式（Formulaic）。模型不仅拉高了天花板，更极大地“拉高了下限（Raising the floor）”，即随便生成一次大概率都不会难听。 - **探索与上限高**：而在一些相对小众、非标准化或结构松散的流派中，模型可能尚未提高质量下限，因此单次尝试可能会出现大量听感糟糕的废案；但模型并没有锁死上限，只要用户愿意多花时间抽卡与反复迭代，依然能从中淘出极其惊艳且富有开创性的作品。 ### `10:00` V5 的质变与版本节奏：为何音乐生成是非线性的 在谈及 Suno V5 带来的跨越式音质提升时，Mikey Shulman 直言这种技术突破（step change）在事前极难精准预测。在音乐 AI 领域，研发投入与产出之间呈现出极强的非线性，甚至内部离线评测的数据提升也无法与真实的受众反馈画等号。在内部测试中，团队可以通过盲测判断一个新模型比旧模型更具优势——例如胜率高出 10% 或 15%，然而两个在评测中胜出幅度相同的模型，推向市场后的实际表现却可能截然不同：用户的热爱程度、实际调用量以及产品增长曲线，往往与实验室里的偏好信号并不完全吻合。Mikey 解释道，这是因为音乐本身是一门极其复杂且充满主观维度的艺术，涉及太多难以量化的细微变量。 从更宏观的研发规划来看，Suno 维持着相当激进的路线图，团队永远在同步探索多个世代的技术储备，甚至在推出 V5 的同时就已经清楚 V6 和 V7 的技术轮廓。然而，在模型训练过程中，工程师总会不断产生新的改进想法与功能欲望，如果一味追求完美，项目很容易陷入停滞。因此，Suno 采取了明确的版本切分策略——人为设定一个截止节点，宣布当前成果即为 V5 并按节奏发布，后续的所有实验性改进则顺延至下一代模型。Mikey 幽默地指出，研发最忌讳的是“闭关修炼两年去造一个拯救全人类的终极音乐模型，结果在这两年里什么都拿不出来”；保持稳定的发布节奏对团队与用户都至关重要。 ### `11:15` 音乐不是 Scaling Law 问题：偏好数据与小模型优势 针对行业内普遍关心的“模型进步到底源自算力与数据的暴力缩放（Scale），还是源自底层前沿算法创新”这一问题，Mikey 给出了一个出人意料的断言：音乐生成在本质上并不是一个 Scaling 问题。在 Suno 内部，模型的参数量其实相当克制。外界常常错误地将在大型语言模型（LLM）领域的经验直接套用在音乐上，误以为模型越庞大、算力堆叠越狠，性能就会自动变强。 在 LLM 领域，行业拥有琳琅满目的评测基准（Benchmarks），尽管人们可以争论某些测试集是否存在缺陷，但基准客观存在，扩大规模（Scale）被证明是高效“刷榜”并沿着能力阶梯往上爬的绝佳途径。然而在音乐世界里，既没有所谓绝对正确的标准答案，也不存在公认的基准指标。音乐是对人类创造性品味的主观拟合，十个人听同一首歌可能会有十种截然不同的评价，因此单纯靠扩大模型规模对解决音乐生成的边际效用很低。反而是保持相对轻量化的模型结构，能够大幅压低推理延迟，在极短时间内把生成的音乐呈现给用户，而这种即时响应对打造优秀的交互体验（UX）具有决定性意义。 ```mermaid flowchart TD accTitle: 语言模型与音乐模型的研发驱动差异 accDescr: 对比大型语言模型依赖基准评测和模型缩放，与音乐生成模型依赖低延迟推理和真实人类偏好反馈强化的循环。 subgraph LLM["LLM 范式：Scaling 驱动"] direction TB L1["标准基准测试\n(客观评测基准)"] --> L2["海量算力与数据 Scaling"] L2 --> L3["爬升能力阶梯\n(巨型参数模型)"] end subgraph Music["Suno 范式：偏好与体验驱动"] direction TB M1["主观审美体验\n(无标准基准)"] --> M2["轻量模型架构\n(极低延迟 UX)"] M2 --> M3["海量真实用户偏好数据"] M3 --> M4["强化学习 (RL)\n反哺模型算法研发"] M4 --> M2 end ``` 这种探索的核心在于前沿研究与真实偏好数据之间的飞轮效应。庞大的活跃用户群为 Suno 提供了海量的偏好对比数据，使团队能够通过强化学习（RL）将模型与人类真实的审美趣味对齐。Mikey 特别强调了一个常被行业忽视的优势：如果没有如此庞大规模的用户偏好数据支撑，Suno 甚至根本无法研发出当前正在使用的算法与技术流派。此外，文本大模型在利用人类反馈强化学习（RLHF）时普遍饱受模型“阿谀奉承（Sycophancy）”的困扰，而在没有所谓事实对错的音乐世界里，偏好数据的利用完全不受这一难题束缚，成为 Suno 最核心的技术壁垒。 - **规模对比**：LLM 依赖巨型参数和算力爬榜，Suno 采用相对紧凑的小模型换取极致生成速度。 - **评测本质**：文本模型存在事实逻辑标准，音乐创作没有标准答案，无法依靠单一基准衡量。 - **数据壁垒**：用户真实偏好数据不仅用于 RL 对齐，更是底层音乐生成算法研发的前提条件。 ### `13:45` 音乐的社会属性：为什么做“AI 版 Spotify”完全走错了路 当主持人 Sonya Huang 将话题引向音乐的文化与社交属性——人们交换混音带（Mixtapes）、将喜爱的歌曲分享给朋友以建立社交纽带时，Mikey 对此表达了极高维度的认同。他指出，音乐在人类文化中占据着远比其他媒介更特殊的地位：首先，相比于电影或文学，普通大众在音乐上的“审美鉴赏力（Taste）”普遍发育得更加充分，几乎每个人都能对音乐风格形成强烈且明确的个人偏好；其次，音乐天生是一种更具共鸣感的社交媒介。在现场音乐节或演唱会上，即便观众只是单纯注视着台上的表演者，人群在声场中共振所产生的社交联结感，也是坐在电影院看电影无法比拟的——哪怕是在一个空荡的放映厅看电影，体验的打折程度也远不及一个空无一人的演唱会。 Mikey 从人类进化心理学给出了自己的见解：在文字发明之前漫长的岁月里，人类祖先正是依靠口耳之间的声音信号进行交流与情感传递，音乐是深深烙印在人类基因底层（DNA）的情感机制。针对外界流传的“AI 音乐不过是在做一个人格剥离（Dehumanize）、批量制造劣质背景声的 AI 版 Spotify”这一质疑，Mikey 坚决驳斥道，这一构想从根本上就是错的。光靠给流媒体套上一层 AI 外壳，绝不可能做出一款超越 Spotify 的产品。 在过去的三十年里，数字音乐由于无处不在而逐渐被边缘化为环境背景音，其社交与情绪联结价值反而在被稀释。Suno 真正感到兴奋并全力押注的方向，不是让音乐进一步走向冰冷的自动化，而是利用 AI 技术去重构、提升并放大音乐在整个人类文化与社会生活中的生态位，让音乐重新变成一种生动的、高互动的社交体验。 ### `15:45` 颠覆 80 亿人的被动消费：创作即娱乐（Creative Entertainment） 当被问及平台用户更偏向创作者还是消费者时，Mikey 揭示了 Suno 最具颠覆性的平台特征：在 Suno 出现之前，全球 80 亿人中绝大部分终其一生都只是纯粹的音乐“被动消费者”，只有极其微小比例的专业人士有能力进行音乐生产，这种极端的单向流动催生了被动与冷漠的消费习惯；而在 Suno 上，**每天有高达 90% 的日活跃用户会亲自动手创作音乐**。 这种行为模式打破了传统内容平台的固有认知——用户在 Suno 上写歌，绝大多数并不是为了把它导出分发到其他平台当作宣传物料，创作本身就是娱乐的目的所在，即“创作即娱乐（Creative Entertainment）”。创造力是人类的天性，亲手创造出某种事物所带来的多巴胺与充实感，是任何被动消费都无法比拟的。Mikey 回忆起自己年轻时组乐队的岁月，他最珍贵的回忆甚至不是登台演出的瞬间，而是在排练室里与朋友们一起碰撞打磨旋律的午后，那种高度投入、富有成效的状态，与“在手机应用里盲目刷上一小时短视频（Doomscrolling）后的空虚与懊悔”有着天壤之别。Sonya 随即笑着分享自己曾是管弦乐团里的提琴手（拉小提琴且拥有绝对音感），对此深表共鸣。 为了更清晰地阐释这一商业逻辑，Mikey 提出了两个生动的类比： - **烹饪类比**：人们在家里下厨做饭，尽管外面的高级餐厅往往能提供味道更佳的佳肴，但自己动手切菜翻炒的过程本身就充满乐趣，品尝自己的劳动果实更具特殊意义。 - **Claude Code 与编程类比**：无数开发者使用 Claude Code 等现代化 AI 编程工具构建小玩具或轻量工具，即使明知这些代码绝不会被部署到 AWS 上供数百万人使用，但享受“构建事物并亲自使用”的过程本身就是最大的快乐。 Mikey 预言，在未来的 10 到 20 年内，由 AI 催生的“创作型娱乐”平台将在各个创意领域全面爆发，因为 AI 的最大价值不仅在于提供纯粹的机器智能，更在于它终于抹平了门槛，让每个人都能在原本被专业壁垒阻隔的领域中自由体验创造的纯粹乐趣。 ### `18:00` 怎样定义“AI 垃圾（Slop）”：5 岁孩子的歌与个人意义 面对互联网上针对生成式 AI 泛滥所引发的“垃圾内容（Slop）”嘲讽与争议，Mikey 给出了极具个人温情的犀利回应。他认为“Slop”这个词如今已经被严重泛化并滥用，成了一个失去具体定义的虚无标签。 他分享了一个昨天的生活细节：他使用 Suno 与自己五岁的孩子一起创作了两首歌曲。如果从客观商业市场的标准来看，全地球 99.999% 的人都绝对不会有丝毫兴趣去听这两首歌，在冷酷的数据指标里甚至可以直接将其归类为无意义的数字垃圾。然而，对于 Mikey 和他的孩子而言，这两首歌承载的亲子互动与情感意义是无价的。“如果有人管这叫 Slop，”Mikey 坦然说道，“那我只能说，我完全不在乎。”在创作即娱乐的新范式下，衡量作品价值的尺子不再只有大众流媒体的播放量，更在于它是否对创造者本人的生活产生了真实而温暖的意义。 ### `20:00` 卧室制作人与上限的突破：从“粗制滥造”到全新艺术家的诞生 当大众创作的门槛被彻底打破时，行业最先泛起的往往是恐慌情绪——人们害怕海量生成的内容会泥沙俱下，淹没所有人的耳朵和流媒体平台。Mikey Shulman 指出，这种现象在音乐史上早有先例：当年笔记本电脑让普通人在卧室里就能做音乐时，大家也曾担忧成群结队的十三岁少年做出的拙劣节拍会毁掉行业。但快进到今天，那次技术平权显然是一件天大的好事。诚然，“劣质音乐”的数量呈指数级上升，但随之诞生的顶尖作品、全新流派和超级巨星也同样水涨船高。AI 工具让数十亿人具备写歌能力，其发展轨迹与卧室制作人浪潮不会有任何本质区别。 在讨论了生成内容的质量下限之后，话题转向了 Suno 所触及的艺术上限。如今，不仅已经有完全由用户通过 Suno 创作的单曲冲上了各大音乐榜单头部、创作者拿到了主流唱片公司的签约合同，更重要的是它赋予了全新视角的创作者发声的机会。Mikey 分享了他最喜爱的一个案例：一位艺名叫 Zania Monet 的诗人，把自己过去十年写下的精美诗篇喂给 Suno，借此把文字转化为动人的旋律，从而找到了一种全新的表达声音，并与庞大的听众群体产生了深刻的情感共鸣。Mikey 强调：“最优秀的音乐永远需要人类的指引。”音乐没有唯一的标准答案，听众爱上一首歌不仅因为旋律，更因为传递这首歌的“信使”（Messenger）；AI 正在帮助世界发现拥有全新音色与故事的新信使。 与此同时，专业音乐人的工作流也正在被悄然重构。当下许多热门榜单的流行歌曲中，其实已经融入了零星的 Suno 片段。那种“要么纯 AI，要么完全不用 AI”的非黑即白论调完全是伪命题，就像如今绝大部分现代流行音乐都会使用数字音频工作站（DAW）与自动修音（Auto-Tune）一样。AI 正在作为一种高效模块被嵌入专业制作流程，推动音乐人更快地探索全新声效，从而持续抬高整个音乐艺术的创造力上限。 - **卧室制作人浪潮的对照**：当年个人电脑普及带来的“卧室 Beat 制作人”曾引发粗制滥造泛滥的担忧，但最终孕育了全球现代流行与电子音乐的繁荣生态。 - **代表性突破案例**：诗人 Zania Monet 将积累十年的诗歌借助 Suno 转化为音乐作品，完成了从文字创作者到音乐艺术家的跨越。 - **混合制作现实**：Suno 已经作为灵感构思和采样工具渗透进多首主流上榜单曲的制作链条中，行业正从“AI 生成整首歌”转向“AI 赋能专业工作流”。 --- ### `22:15` 与华纳音乐的合作：打破AI与传统音乐的对立，重构25年未变的数字体验 音乐产业向来被视作科技初创公司的“法务雷区”，任何轻率闯入者都会遭遇唱片巨头挥舞着版权利刃的围攻。然而，Suno 刚刚与华纳音乐（Warner Music Group）达成了一项标志性的合作与和解协议。对此 Mikey 澄清道，外界常常错误地认为技术公司天然仇恨传统音乐巨头，甚至等着看唱片公司关门大吉（“The record labels are cooked”），但这种看法完全脱离现实。在 Mikey 眼中，唱片公司是世界上最重要的文化机构之一，它们不仅深谙音乐与文化脉搏，更拥有发掘、培养能与全球数十亿人共鸣的超级巨星的核心能力。 Mikey 认为，如果未来分裂为“AI 音乐世界”和“非 AI 音乐世界”，那将是整个行业的悲剧。把用户强行切分到不同平台、割裂体验不仅毫无意义，而且会伤害整个音乐生态。Suno 与华纳联手的核心诉求，是共同开发过去从未存在过的全新互动产品，让粉丝能以前所未有的深度与喜爱的歌手互动。这种体验对各方而言都是正和博弈（Positive-sum）： - **对于艺术家**：获得了一种全新的高黏性互动媒介来经营粉丝社群； - **对于粉丝**：不再只是单向收听，而是能够通过音乐创作的形式与偶像隔空协作； - **对于版权方**：在流媒体增长放缓的背景下，开辟出极具潜力的全新商业化变现模式。 这场合作背后承载着更大的愿景：过去的 25 年间，人类消费数字音乐的方式本质上停滞不前——从 Napster、iTunes 到 Spotify，大家不过是一直在收听静态的音频流。数字化体验已经整整四分之一个世纪没有发生过底层革新，而 Suno 认为音乐产业早已到了迎来全新互动形态的历史转折点。 --- ### `24:45` 科切拉音乐节的未来想象：万人同频共创的“宗教式”互动现场 当被问及何时能在科切拉（Coachella）等顶级音乐节的主舞台上看到 Suno 时，Mikey 笑着透露，其实台下的很多伴奏音轨里可能早就有 Suno 生成的元素了。但他真正期待的不是后台的技术渗透，而是主舞台上全民参与的互动演出。他许下一个愿景：希望在接下来的 12 个月内，行业能迎来第一场真正意义上的“双向交互式演唱会”，台下的成千上万名观众将不再只是挥舞荧光棒的旁观者，而是能与台上的音乐人实时共同制作一首新歌。 ```mermaid flowchart TD accTitle: 传统演唱会与 Suno 互动演唱会模式对比 accDescr: 对比传统单向音频播放与观众共创双向互动的演唱会模式差异 subgraph Legacy["传统音乐消费模式 (25年未变)"] ArtistOld["舞台艺人 / 单向输出"] --> Stream["静态音轨 / 扩音广播"] Stream --> FansOld["台下观众 / 被动倾听"] end subgraph Future["Suno 倡导的共创互动模式"] ArtistNew["舞台艺人"] <--> Engine["Suno 实时共创引擎"] FansNew["现场万人观众群体"] <--> Engine Engine --> Output["实时动态共创圣歌"] end ``` 这种构想源自 Mikey 自身极其震撼的演示经历。每当他面对几百甚至上千名观众现场演示 Suno 并引导全场一起做出一首歌时，那种群体共振的氛围“近乎具有宗教感”（Almost religious）。人类数千年来在许多宗教仪式中都会通过大规模的齐声吟唱和群体合唱来感受崇高与连接，而这种震撼的情感纽带完全不应该被局限在宗教场所中。音乐节本身就是成千上万人为了情感共振而聚集的狂欢场域，利用生成式技术让成千上万互不相识的人现场写下彼此的故事并听见属于他们的合唱，将彻底颠覆线下音乐现场的情感体验。 --- ### `26:30` 消费级产品的护城河：为什么只做模型挡不住Google的围堵？ 在探讨了模型底层与文化愿景后，话题转向了应用层的产品构建逻辑。Mikey 敏锐地指出，目前消费级 AI 领域普遍缺乏足够的创新，但普通消费者对产品粗糙边缘（Rough edges）的容忍度极低。企业级软件的不良体验可以由公司意志或老板买单，但消费级用户是为了“好玩”自己掏腰包，任何割裂感都会迅速导致流失。因此，团队必须将绝大部分精力投入到能够持续取悦用户的 UI、UX 和整体交互体验中。 更现实的原因在于，单纯依靠模型能力根本无法构建稳固的长期商业护城河： - **算力与资本的非对称竞争**：科技巨头如 Google 已经在全力研发音乐生成模型。尽管 Suno 当前的模型效果远超对手，但 Google 拥有无限的算力与资本储备，在纯粹的模型参数与训练投入上他们可以一周七天不间断地砸钱，迟早会在模型基底层面抹平差距。 - **组织价值观的锚定**：Suno 内部最核心的一条公司价值观是“我们只是一家音乐公司”（We're just a music company），甚至从很多角度来看，Mikey 并不希望把公司定义为纯粹的科技公司。这种文化自觉确保了团队永远不会为了技术而炫技，技术存在的唯一目的就是“取悦普通人”。 --- ### `28:30` 关键的产品决策与逆向思考：逃离Discord、专注完整人声叙事与自回归架构 在 Suno 快速崛起的过程中，Mikey 与团队做出了数个极其反常识、甚至违背当时行业主流的产品抉择： 第一个决策是火速逃离 Discord。起初 Mikey 以为 Suno 会像 Midjourney 一样在 Discord 社区里沉淀相当长的时间。但在 2023 年底，他们上线了一个非常简陋、功能并不健全的 Web 网页版。结果仅仅过了五天，90% 的日活流量便从 Discord 全面倒戈涌向网页端。这一压倒性的数据信号让 Mikey 迅速认错并果断转向 Web 产品的全面深耕。 ```mermaid flowchart LR accTitle: 行业主流与 Suno 逆向产品技术抉择对比 accDescr: 展现竞品选择扩散模型与短片段音频，对比 Suno 选择自回归、人声故事与全曲架构的路径 subgraph IndustryRoute["行业主流方案"] Diff["扩散模型 (Diffusion)"] --> ShortClip["10-12秒高保真片段"] ShortClip --> BgMusic["器乐纯音乐 / 背景BGM"] end subgraph SunoRoute["Suno 逆向战略抉择"] AR["自回归架构 (Autoregression)"] --> LongClip["3~3.5分钟完整曲式结构"] LongClip --> Vocals["带歌词的人声故事叙事"] end ``` 而更大、更具争议性的两大逆向豪赌，则直接确立了 Suno 的行业地位： 1. **坚决押注人声与歌词（Vocal & Lyrics），而非纯背景音乐**：当时大部分音乐生成研究都在做没有歌词的环境伴奏，因为生成带人声的歌曲技术难度呈指数级增加。但 Suno 认为一首歌的核心在于叙事，“歌曲即故事”，无人声的背景音乐无法打动人心。正是攻克了人声这一最难的硬骨头，为 Suno 铸就了极深的初期壁垒。 2. **坚决押注完整歌曲（Full Songs），宁可牺牲初期的音质清晰度**：早期所有竞品都在利用扩散模型（Diffusion）追求生成一段 10 到 12 秒的高清逼真音频，音质极其清脆；而 Suno 则坚决采用自回归模型（Autoregression），以损失初始音频清晰度为代价，直接硬拔生成 3 到 3.5 分钟带有起承转合的完整歌曲。在很长一段时间里，Suno 的音质杂音明显，行业内只要听一秒钟就会嘲笑“这声音听起来像垃圾，肯定是 Suno 做的”。但 Mikey 团队坚定地认为：一个拥有完整结构的生动故事，永远比一段音质完美的无声背景音更为震撼。这项完全由“产品与艺术体验”驱动的技术路线抉择，最终让 Suno 跑赢了行业。 在对话的尾声，主持人 Sonya Huang 提到了 Suno 已经达到了令人瞩目的 3 亿美元 ARR（年化运行营收），印证了这一系列逆向产品决策的巨大商业势能。 ### `30:00` 社交共创与用自己的声音表达：Suno 的未来 12 个月重心 Mikey Shulman 坦言，Suno 目前依然处于非常早期的阶段，“大多数人甚至还不知道我们的存在，产品本身也还相当粗糙，未来还有巨大的施展空间”。在规划接下来的产品演进时，他明确指出了团队将在未来 12 个月全力聚焦的两大核心方向。第一项重心是将音乐制作转变为一种更具深度的社交互动体验。在 Mikey 看来，音乐天然就具备极强的社交属性，人们不仅应当互相分享成品音乐，更应该跨越时间和空间一起创作。这种共创既可以是实时的，也可以是异步的。他描绘了一个未来场景：未来用户分享给好友的可能根本不是一首定型的歌曲，而是一个“歌曲模板”，好友可以直接在此基础上加入自己的灵感与即兴演绎（riff on）并回传过来，形成双向的协同创作；甚至用户未来还可以借助这种模式，使用心仪艺术家从未公开发布过的旧素材进行跨时空的合作共创。 另一大重心则是赋予用户在音乐中表达自我的能力，让听众在作品中倾注真实的个人印记。Suno 近期上线的一项重磅功能正是支持用户使用自己的声音进行演唱与生成。Mikey 强调，当一个人在生成的歌曲中听到自己的原声时，对这首歌的心理归属感和情感依恋会产生质的飞跃。更奇妙的是社交传播层面的共鸣：“如果我发给你一首歌，你在里面能清晰听到我的声音，这首歌在你心中引发的共鸣，将远远超过任何一把虽然完美但毫无辨识度的虚拟嗓音。”因为人类的耳朵天生就对熟悉的声音极其敏感，让音乐充满个人真实的印迹，将彻底颠覆人与音乐生成工具之间的情感纽带。 ```mermaid flowchart TD accTitle: Suno Product Focus accDescr: Suno's two major product focus areas over the next twelve months subgraph Social["社交共创 (Social Co-creation)"] A["歌曲模板 / 创作切片"] --> B["异步/实时即兴互动 (Riffing)"] B --> C["好友协同或与艺人作品共创"] end subgraph Identity["自我表达 (Self-Expression)"] D["用户原声采集与融合"] --> E["极高情感归属与辨识度"] E --> F["高度共鸣的熟人社交分发"] end ``` - **社交共创机制**：突破传统的单向试听与转发，探索基于音轨模板（Template）与片段的即兴接力（Riffing），支持用户之间或粉丝与艺术家之间的异步/同步协同制作。 - **声音认同效应**：人类听觉神经对熟悉的人声有极强的敏感度；融入个人真实嗓音后，歌曲的情感黏度显著超越无特征的纯 AI 完美声线。 --- ### `31:30` 重振 MV 叙事艺术：从背景音乐到前台沉浸式视听 谈及音乐媒介的延展，Mikey 表达了自己对音乐录影带（Music Video, MV）长久以来的热爱。他回忆起自己看着 MTV 频道长大的童年，并感叹当下的内容生态中真正优质的音乐录影带实在是太少了。在他眼中，MV 与当今充斥网络的各类“背景音乐（BGM）”有着云泥之别：“一个真正的音乐视频是在收紧整首歌的结构并讲述一个完整的故事，而后者仅仅是随手垫在某个 YouTube 视频底下的附属背景音。” Mikey 毫不掩饰自己对叙事型 MV 的推崇以及对低质背景音乐的乏味感。他强调，Suno 绝不希望让音乐永远沦为生活中的陪衬或边缘背景，而是立志于将用户重新拽回到音乐的核心地带，让音乐成为注意力焦点的娱乐体验。为了实现这一愿景，Suno 已经在内测一款视频生成产品（Video Product Beta）。该功能正处在测试阶段，而抢先体验的用户已经表现出了极高的热情与喜爱。 - **MTV 时代的视听叙事**：真正的 MV 致力于服务歌曲意境并构建独立故事线，具备紧凑的情感张力，而非作为短视频的次要装饰。 - **拒绝“背景化”**：Suno 的产品哲学是让音乐从被动的后台播放（Background）重回台前的主动沉浸（Foreground）。 - **视听融合进展**：Suno 内部的视频功能正处于 Beta 阶段，旨在帮助创作者一键构建与生成音乐高度契合的可视化故事。 --- ### `32:35` 消费级 AI 创业者的缺失：做“从未可能之事”与机器人时代的意义追寻 Sonya Huang 随即抛出了一个行业观察：当下为什么极少有 AI 创业者投身消费级（Consumer）赛道，反而是全行业一窝蜂涌向企业级（Enterprise）市场？甚至连 OpenAI 近期都在战略调整中放慢或关闭了 Sora 等面向大众的消费级产品线。对此，Mikey 幽默地反问作为职业投资人的 Sonya，并给出了自己的直觉判断：“我的理论是，消费级产品本身就是更难做的，而企业端有大量显而易见、明确待解的商业痛点。老实说，消费端竞争更少，我乐见其成。” Sonya 深入拆解了背后的根源。她认为，AI 能够如何自动化现有的业务流程是显而易见的工程问题，但要想描摹 AI 如何潜移默化地融入人类的“玩乐、娱乐与创造方式”，则需要极其罕见的想象力与原创力。大多数人一聊起“AI 音乐”，第一反应就是做一个“AI 版的 Spotify”，而这种缺乏想象力的设想“听起来就糟糕透顶”。 Mikey 对此深表赞同。他指出，Suno 团队最大的兴奋点与内驱力，从来都不是去加速或自动化某个已有的现成事物，而是“去做那些在今天之前根本不可能实现的事”。尽管自动化已有流程具有巨大的商业变现价值，但探索未知显然要有趣得多。Mikey 进而引发了一个充满哲学意味的终极追问：“试想一下，当未来所有机器人和算法替我们完成了所有繁重的工作后，我们该拿自己所有的闲暇时间来做什么？大家绝不会想成天无所事事地刷无聊的短内容，而是会渴望去从事具有创造性、能让自己感到充实且富有生产力的事情。” | 维度 | 企业级 AI 路径 (Enterprise AI) | Suno 的消费级/创作者路径 (Consumer / Creator AI) | |:--- |:--- |:--- | | **核心驱动力** | 现有业务流程的降本增效与任务自动化 | 创造历史上从未可能出现的人类交互与娱乐方式 | | **思维惯性** | 遵循既有模式，解决显而易见的已知商业问题 | 拒绝“AI Spotify”式存量思维，开辟全新主动创造场景 | | **终极价值** | 释放人类劳动生产力 | 在自动化时代为人类提供自我实现与情感表达的充实感 | --- ### `34:08` 结语：重新定义 AI 时代的创作者层 在对话的尾声，Sonya 代表红杉资本向 Mikey 表达了由衷的敬佩与赞许。她回顾了 Suno 从最初与 Harrison 等联合创始人在 Discord 社区低调起步，一路狂飙突进，最终成长为一家真正定义了 AI 时代全新“创作者层（Creator Layer）”与主动娱乐平台的头部音乐科技公司。Sonya 感叹，能够全程见证这段非凡的创业历程令人心潮澎湃，Suno 的探索不仅赋予了大众全新的创造权利，也树立了消费级生成式 AI 的全新标杆。Mikey 则向 Sonya 致以真诚的感谢，并表示这场畅谈非常尽兴，双方在愉悦的互致谢意中结束了整场对话。 ## Notable quotes > `00:00` 在两个看似毫无关联的领域交汇处探索，往往蕴藏着巨大的历史机遇。 — Mikey Shulman > `02:40` 数学本身没算错，只是我们找到了用极高效率压缩音频的方法。 — Mikey Shulman > `05:00` 那么未来就不可能通过 Suno 诞生下一个 Skrillex。 — Mikey Shulman > `10:00` 闭关修炼两年去造一个拯救全人类的终极音乐模型，结果在这两年里什么都拿不出来。 — Mikey Shulman > `18:00` 如果有人管这叫 Slop，那我只能说，我完全不在乎。 — Mikey Shulman > `20:00` 最优秀的音乐永远需要人类的指引。 — Mikey Shulman > `22:15` 如果未来分裂为“AI 音乐世界”和“非 AI 音乐世界”，那将是整个行业的悲剧。 — Mikey Shulman > `26:30` 我们只是一家音乐公司。 — Mikey Shulman > `28:30` 一个拥有完整结构的生动故事，永远比一段音质完美的无声背景音更为震撼。 — Mikey Shulman > `30:00` 如果我发给你一首歌，你在里面能清晰听到我的声音，这首歌在你心中引发的共鸣，将远远超过任何一把虽然完美但毫无辨识度的虚拟嗓音。 — Mikey Shulman > `31:30` 一个真正的音乐视频是在收紧整首歌的结构并讲述一个完整的故事，而后者仅仅是随手垫在某个 YouTube 视频底下的附属背景音。 — Mikey Shulman > `32:35` 试想一下，当未来所有机器人和算法替我们完成了所有繁重的工作后，我们该拿自己所有的闲暇时间来做什么？大家绝不会想成天无所事事地刷无聊的短内容，而是会渴望去从事具有创造性、能让自己感到充实且富有生产力的事情。 — Mikey Shulman ## Mentioned Mikey Shulman, Sonya Huang, Sequoia Capital, Suno, Suno V5, Spotify, Claude Code, Warner Music Group (华纳音乐), Coachella (科切拉音乐节), Harvard University (哈佛大学), Daniel Nadler, Kensho, Harrison Chase, LangChain, Midjourney, Discord, Skrillex, Zania Monet, Napster, iTunes, Google, MTV, YouTube, OpenAI, Sora, AWS ## Video description Most music platforms assume you're a listener. On Suno, 90% of daily users make something. Founder and CEO Mikey Shulman explains why that flips the  model: the act of creating IS the entertainment, with closer parallels to gaming and Claude Code than to Spotify. He breaks down the technical bets that got them here — modeling raw sound waves instead of encoding music theory, choosing autoregression over diffusion to prioritize full songs over crisp clips, and why music isn't a scale problem the way LLMs are. He also shares why partnering with Warner matters more than disrupting the record labels, what a truly interactive Coachella might look like, and why he thinks the digital music experience is finally due for its first real change in 25 years. Hosted by Sonya Huang, Sequoia Capital [Source](https://pscrb.fm/rss/p/traffic.megaphone.fm/CPUAI5617471569.mp3)

[0:00] Mikey Shulman: 在两个看似毫无关联的领域交汇处探索，往往蕴藏着巨大的历史机遇。 [2:40] Mikey Shulman: 数学本身没算错，只是我们找到了用极高效率压缩音频的方法。 [5:00] Mikey Shulman: 那么未来就不可能通过 Suno 诞生下一个 Skrillex。 [10:00] Mikey Shulman: 闭关修炼两年去造一个拯救全人类的终极音乐模型，结果在这两年里什么都拿不出来。 [18:00] Mikey Shulman: 如果有人管这叫 Slop，那我只能说，我完全不在乎。 [20:00] Mikey Shulman: 最优秀的音乐永远需要人类的指引。 [22:15] Mikey Shulman: 如果未来分裂为“AI 音乐世界”和“非 AI 音乐世界”，那将是整个行业的悲剧。 [26:30] Mikey Shulman: 我们只是一家音乐公司。 [28:30] Mikey Shulman: 一个拥有完整结构的生动故事，永远比一段音质完美的无声背景音更为震撼。 [30:00] Mikey Shulman: 如果我发给你一首歌，你在里面能清晰听到我的声音，这首歌在你心中引发的共鸣，将远远超过任何一把虽然完美但毫无辨识度的虚拟嗓音。 [31:30] Mikey Shulman: 一个真正的音乐视频是在收紧整首歌的结构并讲述一个完整的故事，而后者仅仅是随手垫在某个 YouTube 视频底下的附属背景音。 [32:35] Mikey Shulman: 试想一下，当未来所有机器人和算法替我们完成了所有繁重的工作后，我们该拿自己所有的闲暇时间来做什么？大家绝不会想成天无所事事地刷无聊的短内容，而是会渴望去从事具有创造性、能让自己感到充实且富有生产力的事情。

[0:00] Mikey Shulman: When you are staying up late playing with the thing and you don't want to go to sleep, it's like a really good sign that that is what you are meant to be doing. [15:40] Mikey Shulman: People are creating music for the fun and enjoyment and fulfillment that comes with being creative. And so that, the creation is actually the entertaining bit. [19:40] Mikey Shulman: The best music will always require human guidance. And it's because, again, music has no right answer. You like a piece of music because of how it sounds and because of the messenger who delivers it. [22:20] Mikey Shulman: The digital musical experience has basically not changed for 25 years. We've just been streaming music for 25 years. And I think music is like due for a new innovation and a new format. [25:35] Mikey Shulman: We decided to make full songs... We optimized for that, even though originally, most technologies just let you make something like 10 or 12 seconds of music... We chose autoregression instead of diffusion because we think that making a song and telling a story is more important than making crisp audio. [29:50] Mikey Shulman: What are we going to do with all our time after all the robots are doing all our work? You're going to want to be productive and fulfilled.

[0:00] Before Suno, basically everybody was a consumer of music. You know, compared to the 8 billion people on the planet, there are very few people who make music, and the rest of us consume it. The crazy thing about Suno is that on any given day, 90% of the users are going to create something. And the thing that's hard to wrap your head around is you're not creating it to go bring it elsewhere, by and large, to do something with it. People are creating music for the fun and enjoyment and fulfillment that comes with being creative. And so that, the creation, is actually the entertaining bit. And that is the big step change. I'm delighted to welcome Mikey Shulman. Mikey is founder and CEO of Suno, which is building a music company or a creative entertainment platform and has been one of the most novel consumer applications I've seen out of AI. And I'm very, very excited to ask you about your journey and what's ahead for Suno. So thank you for joining us today. Thank you for having me. I'm excited. OK, awesome. I want to start your background because it is very very unexpected You went from a physics PhD at Harvard I think quantum computing with solid state spins to building the largest AI music company in the world. What insight connected those two things for you? You know, I don't know how, I don't know, like on paper, I guess I have no business building a consumer entertainment company, but a lot of people went from physics into AI, just like, You know, 30 years ago, a lot of people went from physics into quantitative trading. I'll be honest, though, like I was an OK physicist only. And there are a lot of better physicists, including one of my co-founders. And I think what I mostly learned is playing at the nexus of two things that don't usually play together is just a massive opportunity in all domains. It can be music and technology. It can be quantum mechanics and low temperature microwave engineering, or it could be whatever else you're going to do. You and I got connected in the very early days of Suno. One of our mutual friends, Harrison Chase, was one of the earliest Suno Discord users, and he was having far too much fun making songs in your Discord. Maybe tell us about the early days of Suno How did it come together Did you set out to build a music company Originally we thought this would actually be too hard And it because you have to rewind This is like pre the chat GPT moment We did some like back of the envelope math we knew we loved audio, but the back of the envelope math told us that actually producing good music, making good music, generating good music was like a couple of orders of magnitude away in terms of compute and model size and capability. And it's because music, sound in general, is like very unwieldy. It's not in discrete bits like text is. And so we actually started building a company that was all around using the same technologies to make sense of audio, not to produce it. And very happily, pretty early on, we had the right breakthroughs. And we realized, oh, we actually can make music. You're pretty good at math. What did you get wrong with your back of the napkin math then? The math was right. We just had some breakthroughs that said, like, it's actually, you don't need that amount of compute. You can make the right technological breakthroughs to, if you want to think about it, basically just compress audio really, really efficiently. and that worked a hell of a lot better than we anticipated So it was like a very nice being wrong moment Not all being wrong moments are so pleasant And to be clear at the beginning the music was terrible but we still stayed up late. I thought it was good. He was one of your first 10 users, I think. He thought he was pretty good. Certainly before we put it on Discord, the music was very terrible. Before we put it on Discord, we could make like 12 and a half second clips that wouldn't always listen to the words you asked them to sing. But we had so much fun doing it. And we thought other people might have fun doing it. And so we kind of took the example of Mid Journey and we said, it's really easy to put a Discord bot out and see, will people enjoy it? And we put it out there and a hell of a lot of people enjoyed it. And that was a really confirmatory moment for us. And so a lot of people told us not to build a music company. It's not the easiest business to work in. Speech is really big. There's a lot of great business use cases for building speech technologies. But when you are staying up late playing with the thing and you don't want to go to sleep, it's like a really good sign that that is what you are meant to be doing. And so that's what we did. I love that. Are you a musician? I am. I play almost every day. I grew up playing a lot of piano and ended up picking up a piano. [5:00] but base around age 12 and playing a lot more of that. Okay, so personal passion point. That's awesome. You know, the revisionist history is that, which is true, is that we used to have jam sessions at our last company in one of my co-founder's basements. And it's true, we had a lot of fun there. It's not why we started the company. Again, we thought it would be too hard to do this. It was just fun. Meaning at Kensho? At Kensho, yes, where I met the great Harrison Chase. The Kensho mafia is like pretty unparalleled. It's Harrison, there's also Daniel Nadler. Sam Whitmore, you, there are a lot of you. There's a lot of us. I just credit Daniel with that, honestly. Daniel is like, I think the best object lesson in what talent density can do for a company. And it was a lot of people with non-traditional backgrounds. It skewed very young, but he was great at finding people and great at convincing them to join. I love that. Okay, so walk us through what happens when like somebody types upbeat 90s hip hop track about a road trip. You get the prompt in, What happens, what is the model doing to be able to pass something back to the user that seems like it's quite special? In some way, it's actually pretty simple. A prompt like that you have to figure out what are the words of this song and we use various llms to do that to make the lyrics and um so it taking basically the cue there is road trip and so like what should this road trip be about and it will probably get it wrong because you didn't give us enough information but that's actually okay you can iterate on it and then you said 90s hip-hop and we tried to expand that out into a set of cues that the model can really understand what is the genre what is the style of this music um and then you put those things together you have a lot of lyrics, you have a lot of styles, and we have our models that are trained to take in all of that information and just produce sound. The amazing thing here is that the models don't know that there's vocals and instruments. It doesn't know what kind of instruments there are. Very early on, it was actually quite obvious to us that the more musical knowledge we give the model, the more constrained it will be in a bad way. And so we actually just model everything as sound. And that's what made it so hard, but ultimately that's what make these things so powerful. So just to be concrete about it, in Western music, there are 12 tones. If you tell the model there are 12 tones, it will only ever produce those 12 tones. You will be forever limited. And if you tell the model there's 200 instruments, those are the only sounds that you'll ever be able to make and you won get the next Skrillex using Suno And so for us it was all about let throw away everything we know about music and let try to do this from scratch And it like, it's just a sound wave. It's just sampled at 48,000 times a second. And it is a continuous, you know, float 32 number. And let's figure out how to model that. And that was a lot of the early breakthroughs that we had to make. But once we did, now you are only constrained by what you can describe and your imagination. That's so cool. Have you found that we've basically just rediscovered, you know, the existing genres of music and the 12 notes and like, have you, I guess, independently seen just that same behavior emerge when you're trying to, I guess, learn music from first principles or have you seen like a different set of capabilities emerge? No, the amazing thing is now we see new things emerge that you never would have thought of. And so most of the time, This looks like blending genres that really have no business going together. And so you'll get, I don't know, trap with a sitar in it, or you'll get country with 808s in it or whatever it is. And again, this is like really empowering people to do the things that are in their heads. And it in a way that would not have been possible without a technology like this or would have been really really hard We see microtonal music It is really inspiring to go and just look at all of the crazy things that people are making A lot of them sound like genres you know, and a ton of them sound like totally strange and bizarre and lovely. That's awesome. That's really cool. Are there certain genres that you're finding your model is better at and certain genres where you're worse at? Definitely. um we are uh i mean i i attempt not to say like good and bad about music other than you know it's sampled well like the the the full bit depth or full sampling rate but um to the extent that you uh can make such generalizations we're like very good at country we're very good at pop music um and i think um the cartoon maybe to have in your head is that there are some genres that are some somewhat more formulaic than other genres. And so perhaps we're like better at those. But I have some sneaking suspicion that for those, it's as much raising the floor as it is raising the ceiling. And for the things where we're less good at it, we've not raised the floor. And so we make a lot of bad music, but we have also raised the ceiling. And if you're willing to go for long enough, you'll find amazing stuff. That's so cool. [10:00] So V5, seems like it was a real step change in quality. What goes into one of those step changes? You know, it's really hard to predict when the step changes happen because it's really nonlinear in both the research inputs, but actually it's not even linear in how much like our testing says the model is better. And so just as an example, we can measure how much one model is preferred to another model. and you may come up with it's 10% preferred or 15% preferred. And you can take two different models and one is 10% preferred or 15% preferred. And the uptake on the other end, how much our users actually love it and use it or how much the product grows when you release it won't necessarily be all that correlated with what the preference signal is. And it's because music is messy and there's lots of other things that go into it. But to take a huge step back, like we have a pretty aggressive research roadmap. And in some weird way, we're always working on this thing. You know, like we know what V6 and V7 are. At some point, there's lots of things that you want to have your model do. There's lots of improvements that you want to make And it almost an arbitrary cutoff of saying like okay this is the break This is what we going to call V5 And everything that comes after is going to go into the next models And almost just to keep it on a steady cadence of when we release things Because what you would hate to have happen is we don release stuff for like two years and we try to make you know the music model to save humanity And that going to come out in two years and we going to do nothing before then Yeah, totally. How much of each of these improvements do you think is just a function of scale, scaling compute, scaling data, and then getting a lot of human preference data back? How much are you guys doing, I guess, more novel research? Music is really not a scale problem. The models are pretty small for a variety of reasons. And I think people will often incorrectly take what they know from LLM land, where models are giant and scale helps a ton, and apply it to music. And I think the cartoon that I have in my head is that in LLM land, there's all of these benchmarks and you can quibble about which ones are flawed and which ones are good. But these benchmarks exist and scale is actually a pretty efficient way to climb up the ladder and just keep doing better and better on the benchmarks. In music, there are no right answers. There are no benchmarks. And so scale is somewhat less helpful in solving it It like a it a messier problem in many ways aligning models to creative human tastes You and I are not going to agree on every song You and I aren't even going to agree. I'll just defer to whatever you say. I don't have I mean, I don't think you want to do that. But so the and the models not being that big actually lets us get you the music quicker, which turns out to be really important for good UX. And so I think a lot of this boils down to research and preference data. And so we gather preference data that lets us align models to what our users like. A really underappreciated thing is how much this preference data actually lets us do research. Like without the scale of preference data that we have, we wouldn't even be able to develop the techniques that we are using. And so there are really some virtuous cycles there in how the product itself keeps getting better just by virtue of having people use it. Interesting. And I guess you can use the human preference data in a much stronger way than the text models because they're all worried about sycopancy, right? And for you, I guess that's much less of a challenge. A hundred percent. A hundred percent. And so I think yeah there just a tremendous amount of edge comes from our ability to understand it do research on it and then RL that back into our models That awesome Okay I want to switch gears a little bit and talk about music as a consumer phenomenon And, you know, you've mentioned consumer creative entertainment platform at the beginning. I want to dig into what that means. Maybe starting with, like, seems like music is just like a cultural, social phenomenon of, you know, I like this song. I send it to my friend. You know, it's a it's a scarce resource. We bond over liking that song and, you know, having the mixtapes, listening to it together, et cetera. And to me, music has always just been like this shared cultural experience. That's what it is. Do you agree with that? And then if so, what does AI music mean for that? I agree with that very strongly. Music has a very different place in culture than other media in a variety of ways. One is actually people's tastes are far more developed in music than they are in other media. Everybody has taste in music in a way that most people don't have taste in film or literature. And the other thing is that music is actually inherently a much more social medium. And if you think about it, how going to a concert is an inherently social thing, even though you're only really looking at the performers and it's because of the people around in a way that what's in the world [15:00] Going to a movie in a movie theater isn't quite as elevated as it would be compared to an empty movie theater, for example. And so I think this is actually largely that humans communicating sonically through our mouths and ears, and therefore music is like a much earlier method of communication than writing. It's like much more in our DNA, I think, music compared to other things. I'm obviously biased. I obviously love music. um i'm not sure uh i think people assume that oh you're just going to have like ai powered spotify and it's going to dehumanize it and um music is going to get terrible that seems to me to be obviously wrong um i don't think you're going to make a better spotify just by powering it with ai and the thing that's really interesting is actually how can we not just change but elevate the place of music in culture and music has this other funny thing that by virtue of being so ubiquitous it ends up being in the background a lot. And the thing that's amazing is that AI can be used to actually change that and to augment how music is perceived in society and in culture, augment how it is used socially because it's actually become less social in the last 30 years. And so that is the corner of the universe that we play in and that we are really excited about Do you see yourselves as today and I guess when you look at your users are people more creators of music Are they more consumers of music or both? This is the crazy thing about Suno. Before Suno, basically everybody was a consumer of music. You know, compared to the 8 billion people on the planet, there are very few people who make music and the rest of us consume it. and that's fine. It tends to cater to passivity. It tends to cater to making it less social and more impersonal. And the crazy thing about Suno is that on any given day, 90% of the users are going to create something. And the thing that's hard to wrap your head around is you're not creating it to go bring it elsewhere, by and large, to do something with it. People are creating music for the fun and enjoyment and fulfillment that comes with being creative. And so that, the creation is actually the entertaining bit. And that is the big step change. It's like, everybody in the world is creative. Being creative makes you feel a certain way. This is like in our DNA. And we are basically using technology to allow everybody to feel those warm and fuzzy feelings. A lot of like the inspiration for me personally for doing this comes from just remembering like the fondest memories that I had or some of the fondest memories that I ever had or making music with my friends Not even performing in bands Like practice was so much fun and you get really close to people making music And it's because it feels really good to be productive in a way that doomscrolling your favorite app for an hour does not feel so good when you're done. I was an orchestra kid, so I was not doing nearly as cool music as you, but I totally agree. What did you play? Violin. Do you still? Yeah. Oh, excellent. I have perfect pitch and I let's just say I'm definitely not playing 12 tones now. So my ears bleed when I play. But I totally agree with you. Okay, so it's like a self expression slash active entertainment platform, some parallels to gaming and some parallels to even like cloud code, right? Absolutely. So I think the thing the thing that's amazing about making music is that you feel good and fulfilled and you enjoy making it and then you listen to it. And there are parallels. So that's what we call creative entertainment. The entertaining part is being creative. It's not that you are being creative for the sake of bringing the piece of content somewhere else. I think you see that in cooking People like to cook even though they can get a better meal at a restaurant And it because it is fun to cook and it is fun to consume what you make And I think a lot of what makes Cloud Code or any of the other platforms so special is that it fun to build things and it fun to use what you build And even though most of the things that I build are definitely not meant to be hosted in AWS and used by millions of people, I actually enjoy the act of building and I enjoy the act of using the thing that I built. And so I predict that like in 10 or 20 years, there will be way more of these creative entertainment things all over the place. And it's because that's actually finally possible. Like that is the thing that AI unlocks. It unlocks lots of intelligence things too, but it actually lets everybody be creative in almost any domain. Yeah. I'm guessing you have an opinion on this. What do you think of the word slop? I do have an opinion. I actually, I mean, my answer is usually it's thrown around without any meaning. And I don't know what people mean by that. I made two songs with my five-year-old yesterday. Is that slop? In the sense that 99.999% of the planet has no interest in hearing that, sure. But that's really meaningful to me. And so if you call that slop, I'm not sure I care. It's an interesting question though, right? [20:00] This has happened before, at least in music, where when way more people start to be able to produce something, people get afraid that it's just going to flood all of our ears and all of the platforms with more content. And this happened when people started to be able to make music on their laptops. You had like a lot of 13-year-olds making beats in their bedrooms. And you fast forward to today, that seems like obviously a good thing. Yeah, there's way more music. It means that there's way more, quote-unquote, bad music. but it also means that there's way more great music and there's new kinds of music that get made and there's new kinds of stars that get made and I see no reason why way more people making music again would be any different from that. I love that. So we talked about the floor the slop floor or the non-slop floor. What about the ceiling? Like what what tell us a little bit about the most incredible things people have been able to do with Suno and I think you guys have had some chart topping hits now. Maybe talk a little bit about that. We have had some chart topping hits. We've had people sign to record deals. We've had people make single songs that chart And that amazing And I think about that as that is a new creator coming with a new perspective that resonates very strongly with people And so that is obviously the ceiling going up My favorite example is Zania Monet who it the stage name of a poet who took all of her beautiful poetry that she had been writing for like a decade and started to make music out of it and found an entirely new voice and an entirely new audience to resonate with her art. And like, yeah, I think this is fantastic. This is people connecting, right? This is like the most personal thing in the world. And when you go and listen to the music, you'll realize it's extremely personal. Like the best music will always require human guidance. And it's because, again, music has no right answer. You like a piece of music because of how it sounds and because of the messenger who delivers it. And we will find new messengers with new sounds, and we already are. And that to me, it's like obviously the ceiling is going up there. The other thing that's really cool is even if they're like, I just know that there are tons of charting tracks that have little bits of Suno in them. They're not entirely Suno. And it's because for the professionals, it's also just an amazing tool to use as part of your workflow. It's not your whole workflow. And so there's this weird thing where I think people incorrectly say it's like either all AI or non AI. And the vast majority of music will have some AI in it just like the vast majority of music today is auto or is digitally produced And again more tools let you push music forward faster let you find new sounds faster To me, this is like obviously the ceiling going higher. That's amazing. Okay, so you chose to go after music, which is like probably the one industry that a lawyer would tell you not to go after, because the minute you're there, you have pitchforks coming after you. I think you just had a pretty landmark settlement partnership with Warner. Can you tell us more about that and what you think that means for the future of collaboration with the existing professional music industry? Absolutely. I think just to back up, I think people incorrectly assume that we hate the existing music industry and especially we hate the record labels. People also expect me to say, oh, the record labels are cooked. I think that's obviously wrong. They're some of the most culturally important institutions in the world. They understand music and they understand music culture. They cultivate and grow stars that resonate with billions of people. And the way I see it it would be a real shame if there were two worlds of music if there were like an AI world of music and a non AI world of music One is it makes no sense because most music will have some AI in it But the other is it just bad for the end user to think about having to separate these things in your head and to have to go to different platforms to have effectively similar usage patterns or interactions And so what I'm most excited about doing with Warner is actually building things together that could never have existed before and building products that let fans interact with their favorite artists and really deepen the artist-fan connection in ways that are just positive sum for everyone. It's great for the artists. They get to engage with their fans. It's great for the fans. They get to feel like they are engaging with their favorite artists through music. It's great for the rights holders. Obviously, this is like a heavily monetizable thing. And it's something that literally could not have existed up until like approximately right now. And my sincere hope is that going forward, we find way more of these opportunities of things to build together that couldn't have existed until today. And just to say it out loud, like the digital musical experience has basically not changed for 25 years. We've just been streaming music for 25 years. And I think music is like due for a new innovation and a new format. And so that's what we're here to do. when they were going to be [25:00] See Suno at Coachella. You probably have already. It's probably in a lot of the music. It's probably in a lot of the backing tracks. I'm sure some of the songs- But I mean like a main stage, like a consumer participation thing. I hope that at some point in the next year, we see a truly interactive concert where the audience is actually able to participate and make music with that artist. One of the coolest parts of my job is if I go and I demo Suno to an audience of hundreds or even a thousand people is making a song with that many people all at once. And it's a very special moment. It's like almost religious. You know, like a lot of religions will do like chanting and singing in large groups. And why does that have to be like confined only to a religious context? Why can't that happen at Coachella where people are already so excited about being together at a festival? And so my sincere hope is that happens in the next 12 months. I love it. Okay, we talked a lot about the model layer and then the, I guess, the cultural experience of making music. I'd love to talk about the application layer product building because I think that also an area where you guys have been really really really innovative What your approach to how to think about building in the application layer I guess a lot to say here The first thing is actually there isn enough innovation for consumers right now but the average consumer is not willing to put up with rough edges. And it's because you're not using this for work. You're using this for fun. You're probably paying for it and not your boss is paying for it or your company is paying for it. And so there just needs to be a bigger emphasis on the actual experience that we deliver to people. Also, just if we're being honest, like it's unclear how much moat exists in only a model. And it's like, I'll just say it, like Google has started to build music models. And while ours are way better today, they're Google and they'll outspend us seven days a week and they can probably catch up on the model side. And so I think it's just really undervalued to invest in the product and the UI and the UX to make sure that you're constantly delighting people. You know, one of our company values is actually like we're just a music company. And in many ways, I don't think of us as a technology company. And this is to make sure that we're not building technology for the sake of building technology. We're building technology for the sake of delighting people. And infusing that in the culture is actually just like really helpful in getting people to realize what the whole point of the company is And so that manifests itself in lots of little ways But from a product building strategy that what it is That's awesome. What are some of the, I guess, consumer product decisions you've made that you're most proud of or that were the most contrarian? A bunch. One that I got wrong was getting off Discord very quickly. I thought we would be on Discord for a while. We got off Discord at the end of 2023, and we released a pretty thin, not full-featured web app, and it took five days for 90% of the traffic to move to the web. So it's just like an overwhelming signal that I had gotten that wrong. Maybe the biggest one and the most contrarian one is at the time a lot of people were experimenting with music. Let me give two, actually. One was to focus on songs and not just background music, to focus on lyrical music. and it's because a song is a story and captivates you in a way that vocalist background music just won't. It was also just way harder and so nobody was really able to do that at the time and so by figuring that out, that was certainly a source of moat. But in hindsight, it not just that we were able to do something hard it that the human voice touches people in a certain way and just makes the product way more delightful than just making background music for fun And then the others also in the same direction we decided to make full songs And so again a song is a story you know it's maybe on average three or three and a half minutes. And we optimized for that, even though originally, most technologies just let you make something like 10 or 12 seconds of music at the expense of sound quality. And for the longest time, our audio was really not crisp. And every single one of our competitors had just way crisper audio. And everybody could hear one second of a Suno song and know, oh, that sounds like crap, that's a Suno song. And to just go all in on that and say like, okay, we're going to make full songs. And yes, they're not going to sound amazing, but they are still going to tell the story instead of making perfectly sounding audio that just is like background music. And so the choice of technologies there was to use autoregression instead of diffusion. But it was really kind of product driven. And to say, like, it's not just that we like autoregression because we have, like, emotional attachment to that technology. It's because we think that making a song and telling a story is more important than making crisp audio. That's so cool. What's ahead for Suno? 300 million in revenue run rates. I mean, you've made it extraordinarily far. What? [30:00] A lot. I think it's really early. Most people don't even know about us. The product is still very crude. There's a lot of room to run. I think you'll see us do a couple of things. One is try to increasingly make it a more social interaction. Music is meant to be social, and so you are meant to be sharing music more with people, but also creating more with people, and that can be both synchronous and asynchronous. And so perhaps one day I'm going to share you not even a song, but a template for a song that you are going to explicitly riff on. and sent back to me, and that is you and I kind of co-creating. Maybe you're going to do that with your favorite artist with some of their old music that never got released, whatever that may be. And I think you will see us go much more in the direction of letting people express themselves in the music. And so like the last big feature we've released is the ability to use your own voice. When you hear yourself in the song, you get so much more attached to it. But actually even more so is when I send you a song and you can hear me in it, that song will resonate much more than some nondescript voice, even if that nondescript voice is very good. And it because the human ear is highly attuned to voices We kind of felt that way And so both of those being more social and letting people pour themselves into the music will be a huge focus for us for the next 12 months I love that. I love music videos. I love music videos. I don't see enough music videos getting made. I grew up watching music videos like on MTV. And there's just a huge difference between a music video, which is tightening the song and telling the story versus background music to put behind whatever YouTube content that I may make. And I love the former and I'm like much less into the latter. And it's because what we would like to do is pull people into music more than they are now and not just have music be forever a background thing. There's actually a video product in beta in Suno right now. And so people really love it. Nice. That's really cool. I can't wait. Why do you think there's so few consumer founders in AI right now. Like what's up with that? Everyone's willing for the enterprise. Like OpenAI just shut down Sora, which to me was, you know, I mean, I understand the reasons, but why do you think there's so few people building in consumer right now I mean I should ask you that You the professional investor I mean my theory is like it just harder and there are a lot of obvious business problems to solve and I happy to have less competition honestly Why do you think it is I think it's very clear to see how AI is going to automate a lot of existing business processes. I think it takes real creativity to dream about how AI can seep into the way that we actually play and create. I think it takes real creativity to see that. and most people when they think AI music probably think AI Spotify, which just sounds terrible. And I think it takes a lot of creativity to do what you're doing. Well, thank you. Yeah, I think like we are much more inspired and motivated by doing something that wasn't possible until today instead of automating or speeding up something that already exists. Not that, again, there's a lot of business value in automating and speeding up something that exists In some sense it is just more fun to do something that could never have been done before Yeah And like what are we going to do with all our time after all the robots are doing all our work You not going to want to do something that could never have been done before Yeah And like what are we going to do with all our time after after all the robots are doing all the work You not going to want to do school for now You're going to want to be, you're going to want to be productive and fulfilled. Yeah, exactly. Awesome. Mikey, thank you for sharing everything about your, your journey to Suno has been so cool. And to see you at the helm of a music company and an active entertainment platform and just, you know, defining what the creator layer means in the world of AI. It's been extraordinary to watch your journey since the original days of Harrison and your Discord. And so kudos on what you've done. And big admirer of you and Suno. Thank you so much. This was a lot of fun. Thank you.