Colossal Uses AI and Quantum Simulations to Shift Biology to Engineering

Colossal利用AI与量子模拟推动生物学走向工程化 Colossal Biosciences计划通过AI和量子模型实现基因编辑自动化，推动生物学研究从90%依赖科学发现转向95%工程化执行。 Ben Lamm详细介绍了Colossal Biosciences如何整合AI、计算生物学及古DNA技术，重建猛犸象、渡渡鸟和恐狼等已灭绝物种的基因组。讨论重点突出了AI的实际应用（例如筛除与毛色相关的有害基因变异），并探讨了伦理框架、多基因编辑以及如恐鸟项目（Moa Project）等与原住民的合作。Lamm还勾勒了一项愿景，即在量子计算、自动化机器人、生物库和物种终极野化的驱动下，推动生物学迈向科学工程化。 Colossal Biosciences 建立了涵盖古 DNA 提取、AI 比较基因组学、基因工程与动物培育的系统化模型，目前已成功将其复活的恐狼个体 Khaleesi 融入 2000 英亩生态保护区的成年狼群中。 (0:04) 面对碎片化严重的古 DNA，团队运用 AI 跨 120 多个猛犸象基因组开展比较基因组学分析，并在渡渡鸟项目中整合 3D 头骨扫描、喙部形态及近百种鸟类基因组构建泛基因组模型。 (4:02) 在恐狼基因变异筛选中，AI 文献挖掘及时发现犬科动物白色被毛基因变异与先天性失明、失聪的关联，使团队得以选择无健康缺陷的替代变异。 (5:02) Colossal 正在推进 AI 模拟、量子计算与自动化实验机器人的融合，预计将传统科研从以发现为主（90% 发现/10% 工程）重塑为以工程为主（95% 工程/5% 发现）的高效研发流程。 (13:56) 针对 2050 年前全球或将丧失多达 50% 生物多样性的严峻形势，Colossal 联手多国政府推出“生物保险库”计划，通过全基因组测序与 ATAC-seq 绘制种群多样性图谱以防范遗传瓶颈。 (17:14)

Colossal Biosciences co-founder Ben Lamm argues that integrating artificial intelligence, multiplex gene editing, and computational biology transforms de-extinction from exploratory science into a scalable engineering discipline. Lamm maintains that while rebuilding extinct genomes accelerates modern conservation, the technology requires strict animal welfare safeguards, deep indigenous partnerships, and clear ethical boundaries. Systems Engineering and AI-Driven Genome Reconstruction (0:04) Colossal structures its operations around a specialized systems model initiated by Dr. George Church, dividing execution across ancient DNA extraction, AI computational biology, multiplex genetic engineering, and preserve-based animal husbandry. Lamm emphasizes that ancient DNA is heavily degraded and fragmented, rendering manual genomic assembly unworkable. For the woolly mammoth project, AI parses more than 120 genomes ranging from 3,000 to 1.5 million years old, comparing them against the Asian elephant's 99.6% identical sequence to pinpoint the specific genes governing mammoth traits rather than altering genomes at random. For species with limited physical specimens, computational requirements become more complex. In the dodo project, where researchers possess only a single genome, Colossal uses a multimodal AI framework. The system processes scientific literature, beak morphology metrics, full MRIs, and 3D cranial scans alongside genomic sequences from approximately 100 related avian species to establish a comparative pan-genome that isolates the genetic drivers of craniofacial morphology. Screening Phenotypic Defects and Shifting to Engineering (5:02) Beyond assembling fragmented sequences, machine learning serves as a safeguard against severe animal welfare defects. In the direwolf project, comparative genomics across specimens aged 73,000 and 12,000 years indicated that the animals possessed white coats. However, cross-referencing genomic data with modern canine veterinary literature revealed that the primary gene variant responsible for white pigmentation also causes hereditary blindness and deafness in wolves and dogs. Guided by AI literature synthesis, researchers selected an alternative variant that achieves the white phenotype without the associated sensory pathologies. Lamm contends that combining AI with quantum computing will enable massive, concurrent biological simulations before executing physical experiments in wet labs. By predicting genetic edits with an estimated 80% or greater success rate, automated robotics can perform physical cell insertions while human scientists focus on high-level validation. Lamm projects this capability will shift research from its historical balance of 90% discovery and 10% engineering to 95% engineering and only 5% discovery. Multiplex Editing Horizons and Ethical Deliberations (6:25) Colossal currently executes more than 75 simultaneous multiplex genetic edits within living cell lines, using AI models to design guide sequences for large genetic payloads. Lamm notes that while these high-throughput editing tools promise therapeutic breakthroughs—such as eradicating inherited childhood illnesses and designing advanced cell therapies—they also present profound moral hazards when evaluated for human germline applications. As multiplex editing advances alongside predictive computing, Lamm warns that society faces difficult demarcations between therapeutic disease eradication and elective genetic enhancements, such as optimizing traits like intelligence. Because germline modifications permanently alter the heritable human gene pool, Lamm stresses the need to collaborate with global regulatory bodies and governments to construct durable guardrails before such capabilities mature. Bio Vaults, Indigenous Consultation, and Conservation Realities (8:53) Acknowledging that de-extinction is far costlier than preserving living species, Lamm argues that technological intervention is nonetheless vital because traditional conservation cannot keep pace with projections that up to 50% of global biodiversity could be lost between now and 2050. Colossal established a $50 million conservation foundation and partnered with international governments to launch dynamic "bio vaults." Unlike static biobanks that simply freeze tissue, these vaults conduct full genome sequencing and ATAC-seq chromatin analysis to map population diversity and forecast inbreeding bottlenecks in critically endangered species like the northern white rhino and vaquita. Lamm underscores that technological capability must be accompanied by community and indigenous consensus. Before initiating work on the moa—a 12-foot, flightless bird hunted to extinction roughly 600 years ago—Colossal partnered with New Zealand's Ngāti Wai and Māori leadership. Because the bird is classified as a sacred taonga, Lamm asserts that technical projects must secure explicit consent, education, and long-term alignment with native custodians prior to implementation. Lamm's 10-year outlook anticipates successfully reviving Colossal's flagship extinct species while deploying its genomic toolsets across global rewilding and wildlife conservation initiatives. However, the operational viability of this vision hinges on whether public policy, ecological safeguards, and cross-border regulatory frameworks can keep pace with rapidly accelerating synthetic biology.

Colossal Biosciences 联合创始人 Ben Lamm 认为，去灭绝技术并非科幻猎奇，而是依托古 DNA 测序、多模态人工智能与自动化湿实验构建的严密工程体系；这一体系正推动生命科学从传统的经验性探索转向高确定性的计算模拟，并为全球严峻的物种灭绝危机提供可迁移的技术解决方案。 多模态AI与古基因组重构系统 (0:04) 根据 Ben Lamm 的介绍，Colossal 将 George Church 的构想落地为由古 DNA、人工智能与计算生物学、基因工程以及动物饲养四大专业核心组成的体系化模型。面对由于年代久远而严重降解断裂的古生物遗传样本，传统人工分析已无法应对海量数据，人工智能因而成为核心支柱。在猛犸象项目中，AI 模型对 120 多个时间跨度从 3000 年前至 150 万年前的样本展开比对，在亚洲象与猛犸象高达 99.6% 的同源序列中精准锁定定义猛犸象特征的极少数关键基因，避免了对活体基因组进行盲目改造。 针对可用遗传数据极度匮乏的物种，团队进一步引入了基于大语言模型框架的多模态 AI 系统。以渡渡鸟项目为例，在仅有一套渡渡鸟全基因组的限制下，该系统通过计算机视觉与自然语言处理技术消化专业文献与鸟喙形态数据，测序约 100 种近缘鸟类，并融合全身核磁共振（MRI）与三维头骨扫描，构建出参照“泛基因组”，从而准确定位调控颅面骨骼形态的基因靶点。 算法风险预警与工程化模拟范式 (5:02) 在恐狼复活实验中，对比 7.3 万年前与 1.2 万年前两个样本的基因组证实了恐狼天然具备白毛表型，但跨物种文献挖掘 AI 随即发出动物福利警报：现代犬科动物中产生白毛的同源基因变异同时携带先天性失明与耳聋的高风险。在算法预警下，研发团队筛选出另一替代变异，既复原了目标毛色，又规避了严重的遗传缺陷。目前团队已实现通过自动化推荐反馈系统，评估不同基因编辑工具组合的交付效率。 结合前沿计算技术，Lamm 提出了合成生物学的范式转变：利用人工智能与量子计算的超大规模并发算力，在计算机内（in silico）对复杂的基因组合与湿实验环境展开数字模拟，预先筛选出成功率超 80% 的编辑方案，再由自动化机器人执行物理细胞操作，人类科学家最终仅需负责成果分析。Lamm 预测，生命科学研发将彻底摆脱历史上一度高达 90% 依赖盲目发现的状态（目前约在 50% 至 60%），最终迈向 95% 由计算模拟驱动的“科学工程”，仅保留 5% 的基础发现环节。 多重基因编辑伦理与原住民共识 (6:25) Colossal 目前已能在活体细胞系中借助 AI 模型设计导向序列，同时执行超过 75 处复合基因编辑。Lamm 指出，随着多重编辑在量子与 AI 驱动下不断提速，该技术在治愈儿童遗传病等治疗领域前景广阔，但也引发了关于生殖系编辑导致基因持久遗传、定制智力等非治疗性状增强，以及重新定义“何为人类”的深层伦理危机。他认为社会已从单纯的技术恐慌转向务实的落地阶段，但必须依赖跨国界、跨组织的监管框架。 为了应对技术争议并重塑信任，Colossal 强调以社区与原住民共识为前置条件。以新西兰恐鸟（Moa）项目为例，这种约 12 英尺高、无翼且对维持林冠植被具关键生态作用的鸟类，在约 600 年前被早期毛利定居者猎杀灭绝。鉴于恐鸟在毛利文化中被视为神圣珍宝（taonga），团队在启动任何技术方案前，必须与毛利部族及 Ngāti Wai 研究机构达成伙伴协议，在获得原住民的明确许可与文化认同后方可推进研发。 新型生物保险库与长远生态愿景 (17:14) 针对外界关于为何不优先投入濒危物种保护的质疑，Lamm 坦承复活绝灭物种的资金成本远超保护现生种群，但科学界预测 2050 年前全球多达 50% 的生物多样性可能丧失，常规保护手段在灭绝速率面前已难以独立支撑。为此，Colossal 设立了 5000 万美元的保护基金，并与国际政府合作推出“生物保险库”（Bio Vault）计划。该机制超越了仅冷冻生物样本的传统做法，引入全基因组测序与基于 ATAC-seq 的染色质可及性分析，绘制种群遗传多样性图谱，精准识别近亲繁殖风险与基因瓶颈。 在十年远期规划中，Colossal 计划不仅实现旗舰去灭绝物种在自然保护区内的野化共处与放归（例如恐狼项目已在 2000 英亩生态保护区实现稳定融合），还将无偿向全球保护项目开放底层技术平台，直接赋能北方白犀牛和小头鼠海豚等极度濒危物种的拯救行动。近期内，团队宣布其禽类基因组学已取得阶段性突破并将在年内公布，持续推进去灭绝技术的管线布局。 尽管 Colossal 展示了以工程化与计算力突破生物保护瓶颈的宏大路线图，但其复活成果在脱离人工看护、重新进入自然生态系统后的长期适应性与生态位冲击仍有待现实检验；正如 Lamm 所言，改变人类的破坏性消费行为远比攻克技术壁垒更加艰难，去灭绝技术究竟能在多大程度上逆转全球物种大灭绝趋势，依然取决于跨国界协作与跨代际伦理监管的持久有效性。

## Background 本内容由 Ai4 频道于 2025 年 8 月 28 日发布在 YouTube 平台。根据页面简介，本次对话为 NBC News 的 Jason Abbruzzese 对 Colossal Biosciences 联合创始人兼 CEO Ben Lamm 进行的炉边访谈，重点探讨如何利用 AI 创新推进合成生物学发展、去灭绝（De-extinction）的具体流程以及在 AI 时代下的战略思考。 ## TL;DR Colossal Biosciences 正通过融合 AI、计算生物学、多模态模型与基因工程，将“灭绝物种复活”从科幻变为工程现实。公司已在恐狼毛色解析与群落融入、猛犸象与渡渡鸟基因组重构等项目中取得突破，并推出了“绒毛鼠”等成果。此外，团队在项目推进中高度重视动物福利、伦理边界与原住民文化合作，并通过设立保护基金与“生物金库”计划，利用前沿技术为全球生物多样性提供终极保障。 ## Atmosphere & style 这场炉边对话气氛轻松而前沿，既有开场谈及科幻预告片时的风趣幽默，又充满了对严谨科学与前沿技术的深刻洞察。主持人与嘉宾的互动流畅自然，Ben Lamm 的阐述展现出极强的前瞻性与使命感，将复杂的生物学工程与理性严谨的伦理思考娓娓道来，给观众带来一种“见证科幻走进现实”的震撼与沉浸感。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=kOaBbptCMK0&t=4s) 复活恐狼的现实进展与 Colossal 的系统化构建 NBC News 的 Jason Abbruzzese 与 Colossal Biosciences 的联合创始人兼 CEO Ben Lamm 展开了一场别开生面的炉边对话。现场首先播放了一段关于“灭绝物种复活”（De-extinction）的宣传预告片，引发了关于版权和科幻走向现实的轻松探讨。谈及最受关注的进展，Ben Lamm 透露，公司刚刚发布了关于恐狼（Direwolf）幼崽的最新动态（"pupdate"）。他们成功将雌性恐狼幼崽 Khaleesi 融入到了包含 Romulus 和 Remus 两只成年雄狼的狼群中。在占地 2000 英亩的生态保护区内，整个融入过程非常顺畅，动物福利（Animal Welfare）得到了充分保障。 针对外界对 Colossal 到底是 AI 公司还是生物公司的疑问，Ben 明确指出，要将十数年前仍属于科幻领域的“灭绝物种复活”落地，单一技术是远远不够的，必须构建一整套完整的系统架构。 ``` +-----------------------------------------------------------------+ | Colossal 灭绝物种复活系统架构 | +-----------------------------------------------------------------+ | [古 DNA 核心] -----> [AI 核心 / 计算生物学] -----> [基因工程] | | (获取古老样本) (基因组重构/比较基因组) (细胞系编辑) | +-----------------------------------------------------------------+ | v [动物饲养与繁育团队] (保护区生态融入与监护) ``` - **恐狼融入成功**：雌性恐狼 Khaleesi 已完美融入 Romulus 与 Remus 的狼群，并在 2000 英亩的保护区内生活。 - **系统核心构成**：包含 AI 核心、计算生物学核心、古 DNA 采购与重构核心、基因工程编辑团队以及动物饲养与保护团队。 ### [02:42](https://www.youtube.com/watch?v=kOaBbptCMK0&t=162s) AI 在基因组重构与比较基因组学中的核心作用 在谈到 AI 的具体应用时，Ben Lamm 表示，没有 AI 就无法完成目前的工作。古 DNA 往往高度碎片化，需要借助 AI 进行重构与比较基因组分析。以猛犸象（Mammoth）项目为例，团队拥有 120 多个跨越 3000 年至 150 万年历史的基因组，AI 能帮助分析出究竟是哪些基因决定了猛犸象的特征（亚洲象与猛犸象的基因相似度高达 99.6%），从而避免盲目编辑。 对于只有单一基因组的渡渡鸟（Dodo）项目，比较基因组学面临巨大挑战。为此，团队建立了一种多模态 AI 框架，融合了计算机视觉（Vision）、深度学习与大语言模型（LLM）： ``` +-----------------------------------------------------------------+ | 渡渡鸟 (Dodo) 多模态 AI 系统 | +-----------------------------------------------------------------+ | 鸟类嘴部形态学 + 100种相关鸟类测序 + 头部骨骼3D/MRI扫描 + 学术文献 | +-----------------------------------------------------------------+ | v [多模态 AI / LLM 综合分析] | v [精准定位头面部形态基因] ``` - **猛犸象项目**：利用 120+ 跨越 3000 年至 150 万年的基因组，精准定位区别于亚洲象（99.6% 相似）的关键基因。 - **渡渡鸟项目**：基于唯一的基因组，结合 100 种鸟类的测序、MRI 3D 扫描以及嘴部形态学数据构建泛基因组（Pan-genome），定位控制头面部骨骼形态的基因。 ### [05:02](https://www.youtube.com/watch?v=kOaBbptCMK0&t=302s) 海量数据解析与动物福利红线：恐狼毛色案例 人类专家难以凭一己之力处理如此庞大的基因与文献数据，而这恰恰是 AI 的强项。在解析恐狼基因时，团队对比了分别具有 73,000 年和 12,000 年历史的两份基因组，证实恐狼实际上是白色的（《权力的游戏》中的设定是正确的，而网路上关于红色毛色的油画并不符合事实）。 然而，AI 系统在交叉比对海量文献时发出了关键警报：如果在基因组中直接引入使毛色变白的特定基因变体，在犬类和现代狼中会导致失明和失聪。借助 AI 的分析，团队避开了这一动物福利隐患，转而采用另一种既安全又能实现相同毛色表型的基因变体。 - **恐狼表型验证**：通过古基因组确认恐狼特征为白色毛发。 - **AI 风险预警**：AI 从文献中识别出某种白毛基因变体会引发盲聋等缺陷，促使团队更换为安全的替代变体。 ### [06:27](https://www.youtube.com/watch?v=kOaBbptCMK0&t=387s) 合成生物学的伦理边界、社会影响与公众沟通 合成生物学、AI 与未来量子计算的融合正在加速基因编辑技术的飞跃。Colossal 目前能够利用自研系统与模型一次性进行 75 个以上的基因编辑，并将大片段载体递送到细胞中。这种技术突破也带来了关于胚胎编辑、种系编辑（Germline Editing）以及消除疾病甚至提高智力的道德风险（Moral Hazard）讨论，迫使人们重新思考“何为人类”。 Ben 提到，随着科技演进，大众对 AI 的态度已从最初的“悲伤、恐惧与怪异”转变为“接受与应用”。为了推进行业规范，Colossal 积极与各国政府、原住民群体及科研机构展开合作。 在回应社交媒体上的互动时，Ben 提到公众经常重复询问关于野生动物保护的问题，甚至有人据此制作了“长毛象聊天机器人”。事实上，公司不仅免费向野生动物保护项目提供技术，还设立了 5000 万美元的保护基金。 - **高通量基因编辑**：Colossal 系统支持一次性执行 75+ 处基因编辑并递送大片段 DNA。 - **社会与伦理参与**：与联邦政府及原住民机构合作，探索合成生物学及种系编辑的伦理边界。 - **保护基金支持**：设立了 5000 万美元的专有基金用于生态保护。 ### [10:00](https://www.youtube.com/watch?v=kOaBbptCMK0&t=600s) 新西兰恐鸟 (Moa) 项目与原住民合作 Ben Lamm 表示，他们有一半以上的精力花在了科普教育、政策沟通和跨国合作上。因为生物绝灭与复活不受地缘政治边界限制，必须获得社会各界的理解与许可。 最近，公司正式公布了**恐鸟（Moa）复活项目**。恐鸟是一种曾生活在新西兰的 12 英尺高、无翼且无法飞行的巨型鸟类，大约在 600 年前被毛利人（Māori）猎杀至灭绝。在正式启动该项目前，团队首先与 Ngāti Wai 研究中心及毛利部落展开沟通，因为恐鸟被原住民视为神圣的宝物（Taonga）。只有在取得原住民的认可与授权后，科学研究才能顺利推进。 - **恐鸟（Moa）特征**：身高达 12 英尺、无翅膀的植食性巨鸟，对森林冠层生态有重要调节作用。 - **原住民协同机制**：与 Ngāti Wai 及毛利部落深度合作，在尊重文化遗产的前提下开展古 DNA 恢复。 ### [12:13](https://www.youtube.com/watch?v=kOaBbptCMK0&t=733s) 前沿探索：AI 闭环反馈、模拟湿实验与自动化机器人 在谈到合成生物学的前沿突破时，Ben 介绍他们正在建立一套基于实验数据的**反馈回路 recommendation system（推荐系统）**。在对细胞系实施大片段插入、小片段敲除或单碱基替换时，不同的编辑方式效率差异巨大。系统通过吸收大量细胞编辑的成功与失败数据，能够针对具体的编辑目标推荐最佳的技术工具路径。 展望未来，Ben 对 AI、量子计算与机器人的三者结合充满期待： ``` +-----------------------------------------------------------------+ | 未来合成生物学研发工作流 | +-----------------------------------------------------------------+ | [AI + 量子计算] --------> 运行干实验室模拟 (80% 概率最优解) | | | | | v | | [实验室自动化机器人] ----> 执行高通量湿实验 (Wetware Work) | | | | | v | | [人类科学家] ------------> 仅对最终产物进行分析与验证 | +-----------------------------------------------------------------+ ``` - **推荐系统**：利用历史编辑数据训练 AI，针对不同细胞系和基因编辑类型自动推荐最佳工具。 - **未来干湿实验融合**：借助 AI 和量子计算在计算机中完成 80% 概率的模拟实验，再由机器人自动执行湿实验，最终由科学家进行高级分析。 ### [13:56](https://www.youtube.com/watch?v=kOaBbptCMK0&t=836s) 量子计算与 AI 融合：从科学发现走向科学工程 主持人 Jason Abbruzzese 追问了关于量子计算与 AI 结合的前景。Colossal Biosciences 联合创始人兼 CEO Ben Lamm 解释道，虽然量子技术在通信和国防领域有着更为引人注目的应用，但对于生物技术而言，量子计算的核心价值在于其能够提供海量的并行算力，实现在真正湿实验（wetware）环境下的超大规模模拟。 通过结合 AI 系统中积累的已知数据，量子模拟能够评估基因编辑的成功概率，并在瞬间并行模拟所有基因组合。例如，若将恐狼（direwolf）的特定基因引入现代狼体内，系统可提前预测并筛查出可能导致失明或失聪的遗传缺陷，最终直接输出一份明确的实验指导报告。Ben Lamm 强调，这将推动整个生物学界完成从“科学发现”向“科学工程”的范式转变。在过去，90% 的工作依赖于探索发现，仅 10% 为工程实施；而未来，依靠量子与 AI 的算力叠加，95% 的工作将变成可控的工程化实施，发现过程则直接在系统内部自动完成。 - **算法与算力要求**：系统通过整合文献数据与实验反馈，预测复杂基因组合的表达结果，从而极大缩短实验周期。 - **范式转移**：生物学研究重心正在从过去“90% 发现、10% 工程”过渡到当前的“50/50”或“60/40”，未来将实现“95% 工程、5% 发现”。 --- ### [15:55](https://www.youtube.com/watch?v=kOaBbptCMK0&t=955s) 近期进展与十年愿景：从旗舰物种到全方位生态复育 在谈及公司近期的研发成果时，Ben Lamm 总结道，Colossal 今年已经通过“绒毛鼠”（woolly mice，其中名为 Chip 和 Dale 的实验鼠状态良好）以及恐狼项目惊艳了世界。虽然今年不会再宣布新的物种，但团队正集中精力推进三大旗舰物种的研究，并预计在年底前公布鸟类基因组学（Avian Genomics）小组的突破性进展。 谈到未来十年的愿景，Ben Lamm 表示，如果一切顺遂，Colossal 希望能彻底复活所有旗舰物种，并与原住民群体、私人土地所有者及各国政府紧密合作，将这些物种重新引入自然栖息地，评估其对生态环境的积极影响。同时，公司开发的技术也将超越单纯的保护用途（如针对北方白犀牛和小头海豚的项目），在全社会实现更广泛的落地应用。 - **近期进展**：已成功推出“绒毛鼠”（Chip 和 Dale）和恐狼相关成果；年底前将在鸟类基因组学方向公布新进展，但不再新增物种发布。 - **十年目标**：完成旗舰物种的复活与野外重引入；与本土社区和政府建立合作；将研发的合成生物学与基因编辑技术进行规模化推广。 --- ### [17:14](https://www.youtube.com/watch?v=kOaBbptCMK0&t=1034s) 应对灭绝危机：生物金库与现代保护技术的终极保障 面对主持人关于这是否意味着“灭绝的终结”的提问，Ben Lamm 给出了极为客观的解答。他指出，重新工程化并复活一个已灭绝物种的成本，远高于直接保护现有物种。然而，考虑到到 2050 年全球可能丧失 50% 的生物多样性，仅仅依赖现有的传统保护手段显然跟不上物种消失的速度。改变大众和消费者的行为模式道阻且长，而推动技术进步则是更为可控的突破口。 为此，Colossal 及其基金会正在发起一项类似于“诺亚方舟”的“生物金库”（Bio Vaults）计划。与传统仅将样本冻存在冰箱里的生物样本库不同，该计划与全球多个政府合作，对物种进行全面的单细胞转录组（ATAC-seq）及全基因组测序，构建群体多样性图谱。通过这种方式，团队可以精准评估现有种群（如仅剩 200 只的某种犀牛）的健康状况、近亲繁殖程度及未来可能遇到的基因瓶颈，为全球生物多样性构筑起一道坚固的技术后盾。 ``` [生物金库 (Bio Vaults) 运作流程图] +-------------------+ +-------------------+ +-------------------+ | 采集物种组织/基因 | --> | 全基因组 sequencing | --> | 构建群体多样性图谱 | | (多国政府合作) | | 与 ATAC-seq 分析 | | (预测基因瓶颈/健康) | +-------------------+ +-------------------+ +-------------------+ ``` - **生物多样性危机**：预测在 2050 年前，地球可能失去高达 50% 的生物多样性。 - **生物金库特色**：超越传统冷冻保存（bio-banking），提供全基因组测序与基因健康分析，提前预防种群近亲繁殖与基因瓶颈。 - **保护理念**：虽然保护现有物种比“去灭绝”（de-extinction）成本更低，但面对急速恶化的生态环境，技术手段是不可或缺的安全保障。 ## Notable quotes > [10:00](https://www.youtube.com/watch?v=kOaBbptCMK0&t=600s) 因为生物绝灭与复活不受地缘政治边界限制，必须获得社会各界的理解与许可。 — Ben Lamm > [13:56](https://www.youtube.com/watch?v=kOaBbptCMK0&t=836s) 我们将推动整个生物学界完成从“科学发现”向“科学工程”的范式转变。 — Ben Lamm > [13:56](https://www.youtube.com/watch?v=kOaBbptCMK0&t=836s) 过去 90% 的工作依赖于探索发现，仅 10% 为工程实施；而未来，依靠量子与 AI 的算力叠加，95% 的工作将变成可控的工程化实施。 — Ben Lamm > [17:14](https://www.youtube.com/watch?v=kOaBbptCMK0&t=1034s) 重新工程化并复活一个已灭绝物种的成本，远高于直接保护现有物种。 — Ben Lamm > [17:14](https://www.youtube.com/watch?v=kOaBbptCMK0&t=1034s) 改变大众和消费者的行为模式道阻且长，而推动技术进步则是更为可控的突破口。 — Ben Lamm ## Mentioned Ben Lamm, Jason Abbruzzese, Colossal Biosciences, NBC News, Ai4, Khaleesi, Romulus, Remus, 恐狼 (Direwolf), 猛犸象 (Mammoth), 渡渡鸟 (Dodo), 恐鸟 (Moa), 毛利人 (Māori), Ngāti Wai 研究中心, Chip, Dale, 绒毛鼠 (Woolly Mice), 北方白犀牛, 小头海豚, 生物金库 (Bio Vaults) ## Video description Ben Lamm, Co-Founder/ CEO, Colossal Biosciences Jason Abbruzzese, Assistant Managing Editor, NBC News Abstracts This session will explore how Colossal makes use of AI innovation to advance synthetic biology and power breakthrough discoveries. Learn how the de-extinction process works, and get an inside look into how Co-Founder and CEO Ben Lamm thinks about the AI era. [Source](https://www.youtube.com/watch?v=kOaBbptCMK0)

[10:00] Ben Lamm: 因为生物绝灭与复活不受地缘政治边界限制，必须获得社会各界的理解与许可。 [13:56] Ben Lamm: 我们将推动整个生物学界完成从“科学发现”向“科学工程”的范式转变。 [13:56] Ben Lamm: 过去 90% 的工作依赖于探索发现，仅 10% 为工程实施；而未来，依靠量子与 AI 的算力叠加，95% 的工作将变成可控的工程化实施。 [17:14] Ben Lamm: 重新工程化并复活一个已灭绝物种的成本，远高于直接保护现有物种。 [17:14] Ben Lamm: 改变大众和消费者的行为模式道阻且长，而推动技术进步则是更为可控的突破口。

[2:42] Ben Lamm: made a mammoth a mammoth [6:25] Ben Lamm: temper tantrum phase [6:25] Ben Lamm: acceptance phase of grief [8:53] Ben Lamm: taonga [17:14] Ben Lamm: Noah's Ark

[0:04] Uh Ben, thank you for being here for [0:06] what uh I'm hoping will be maybe the [0:08] weirdest uh uh fireside [0:10] >> applications of AI using de-extinction. [0:13] >> Yeah, I mean deextinction a word. I'm [0:15] not even sure when that first came onto [0:17] the scene, but and we just saw a good [0:20] teaser trailer, [0:21] >> which we had all the rights for the [0:22] >> all the I'll check with our legal [0:24] department. [0:24] >> I don't want to get like yeah some ex [0:27] nerds on it. as an NBC Universal [0:29] employee. I that's that's good to hear. [0:31] Um well, when people hear deextinction, [0:33] I think obviously science fiction is the [0:35] first thing that comes to mind, but [0:36] we're now starting to see it in the real [0:38] world. The first thing I need an update [0:40] on is how are the direwolves doing? [0:42] >> Good. We just gave our first uh pupdate [0:45] yesterday. Uh and so we uh we we showed [0:47] uh we I think we launched it with USA [0:49] Today and a few others. I think NBC we [0:51] sent it to as well, which I think [0:52] they're covering, which is exciting. uh [0:53] and uh we just integrated Khesi uh our [0:56] girl into the pack with Romulus and [0:58] Remis and it went well and so one of the [1:00] things that you know this isn't [1:01] obviously AI related but animal welfare [1:04] is like key to everything that we do and [1:06] so the integration was a little bit [1:07] nerve-wracking right it's like we're [1:08] we're integrating a female wolf into a [1:11] pack of two males that are older and it [1:14] went great and they're all interacting [1:16] great they're on a 2,000 acre ecological [1:18] preserve and uh now they're completely [1:20] integrated as a pack which is awesome [1:22] And I I I think they're a great real [1:24] world example of of what you guys are [1:26] doing, which is, you know, a kind of [1:29] science that even as of 10 to 20 years [1:32] ago was again relegated to to science [1:34] fiction. How did you go from [1:38] whatever the first idea was for doing [1:40] this to having actual real living [1:43] >> direwolves [1:44] on a on a preserve? [1:47] >> We uh well, this is really all George [1:48] Church's fault. So it it was his idea in [1:51] terms of like how we could use genetics [1:52] and and AI and computation biology to [1:54] bring back species as well as use those [1:57] technologies for you know healthcare as [1:59] well as conservation. And what's [2:00] interesting is uh we had to build the [2:03] entire system. So sometimes people are [2:04] like well you guys raised a lot of money [2:06] uh are you guys just an AI company? How [2:07] are you guys looking at this? But [2:09] fundamentally we had to build the entire [2:10] system. So we've got an AI core, we've [2:13] got a computational biology core, we've [2:14] got an ancient DNA core that goes out [2:17] and procure samples, and then we have to [2:19] use all these technologies to take those [2:20] samples, compare them to the closest [2:22] living relatives, and then engineer them [2:24] into cell lines using genetic [2:26] engineering. Uh, and then we have an [2:28] animal husbandry team. And so there's [2:30] all these different components that we [2:31] had to go build for this systems model [2:33] for deextinction and conservation. [2:35] >> And, and you touched on this briefly, [2:36] but let's get like down into the [2:38] nitty-gritty. How is AI [2:40] being used in this process? [2:42] >> We couldn't do like to your point uh we [2:45] couldn't do our job without AI. So we're [2:47] using AI in the uh ancient DNA [2:50] reconstruction of the genome. So when we [2:51] get genomes are extremely broken. Uh [2:54] like so for the mammoth project we have [2:56] over 120 genomes ranging from 3,000 [2:58] years old to 1.5 million years old. We [3:01] then use AI to actually do all the [3:03] comparative genomics uh which is pretty [3:05] interesting. We have to try to [3:06] understand what genes made a mammoth a [3:07] mammoth. Because for us, if you look at [3:09] an animal and you look at an Asian [3:11] elephant, which is 99.6% the same as as [3:14] a woolly mammoth, we want to identify [3:16] the genes that really made a mammoth a [3:18] mammoth, right? And so we don't want to [3:19] go in and just edit everything. We want [3:21] to be really thoughtful about it. And so [3:22] we're using AI in that process, too. [3:24] We're even using AI. Uh this is kind of [3:26] weird. Not every single project we have [3:28] unlimited genomes for. Like the Dodo [3:30] project, we only have one genome. [3:32] >> The Dodo project being [3:33] >> the Dodo project. We're bringing back [3:34] the [3:34] >> bring bring back the job. Yeah. And so [3:35] we only have one genome and so we don't [3:37] have like 120 plus genome. So, so it's [3:39] hard from a comparative genomics [3:40] perspective and uh so this is [3:43] interesting where we're looking at kind [3:44] of like converging AI and using kind of [3:46] this mo multimodal approach where we're [3:48] taking vision uh artificial intelligence [3:51] and even natural language processing and [3:53] pulling into kind of this kind of uh uh [3:55] system that we built using kind of this [3:57] LM framework from research where we're [3:59] insert we're pulling in all the data [4:01] from all this bird beak morphology uh [4:04] and then we're also doing all the [4:05] sequencing of about a hundred different [4:07] species of birds and that that are [4:09] related and some closely and some not [4:11] closely related to the dodo. And then we [4:13] actually did MRIs and we did full 3D [4:15] scans of every single uh bird in in [4:18] their skull that that in that kind of [4:20] group so that we could build a pan [4:22] genome so then we could use our AI [4:24] system to understand okay how do we [4:26] isolate genes that we think would change [4:28] the cranial facial morphology of the [4:30] bird. So, so we have to so we're [4:33] building these kind of like uh systems [4:34] that kind of uh cross vision, cross deep [4:37] learning and cross uh even uh LLMs where [4:40] we're pulling in data from all the [4:41] scientific research to really understand [4:44] you know where we where we can even uh [4:46] start our editing for the for that [4:47] because we only had one GM. Well, I [4:49] think you kind of just touched on where [4:50] I wanted to go next, which was, you [4:51] know, why couldn't humans be doing that [4:54] kind of thing to ask it very simply, [4:56] what is challenging about what you're [4:59] doing that AI is good at? [5:02] >> Yeah. Well, I mean, it's it's just a ton [5:03] of data, right? And so, um, uh, and it's [5:07] hard for us to parse it. So, so, for [5:09] example, when we did the, uh, [5:10] comparative genomics for the direwolf, [5:13] we had two direwolf genomes. One was [5:15] 73,000 years old, one was 12,000 years [5:17] old. Uh, we saw that, you know, [5:18] direwolves were white. We found out that [5:20] they were white. We looked at it. We [5:21] looked the day they're white. Game of [5:23] Thrones got it correct. Uh, lots of [5:25] people on social media yelled at us [5:27] because they saw a picture that someone [5:28] painted of them as red and they're like, [5:29] "No, we saw a picture that someone [5:31] painted and they were red uh in 2015, [5:33] right?" And I was like, "Well, they [5:34] weren't alive in 2015." And so, but what [5:36] we found, and this is where AI was [5:38] really helpful to us, what we found is [5:40] is is in the data, it's like, "Go make [5:42] this genome, this edit in the genome to [5:44] make them white, right? And here's the [5:46] gene specifically that that made them [5:48] white. But then when we pulled in all [5:49] this literature, we actually found in [5:52] dogs and in other wolves, some of those [5:54] same gene variants actually caused [5:56] blindness and deafness. And that's not [5:58] something that that the computational [6:00] analysis showed us in the data. And so [6:02] pulling in all this extra data and [6:04] having AI parse it, it kind of gave us [6:06] this red flag of saying, hey, if you go [6:08] make this change, there could be an [6:09] animal welfare issue to it. And so we [6:11] then use that data said okay we're going [6:12] to have the same outcome but we're going [6:14] to use this gene variant over here [6:15] because it's safe and and that's [6:17] something that you know the data of us [6:19] just looking at the raw data without [6:21] leveraging AI to kind of look at all the [6:23] ingestable data uh would we would have [6:25] known. [6:25] >> Yeah. Uh now you you kind of just [6:27] touched on where humans start entering [6:29] the process and the decisions they have [6:31] to make. Um, what kind of work does your [6:33] company do and do you do thinking about [6:36] any of the moral or ethical or societal [6:38] ramifications of these technologies? [6:40] Obviously, I think people hear them, [6:42] including myself, and they start going, [6:45] >> "That's that's that's scary stuff. What [6:47] else could it be used for beyond doing [6:50] things like [6:51] >> it is it is crazy." And for us as it [6:54] relates to synthetic biology, we're [6:56] working with the federal government. [6:58] We're working with indigenous people [6:59] groups. We're working with a lot of [7:00] different collaborative groups around [7:01] the world to actually think about how we [7:03] build frameworks for leveraging [7:04] synthetic biology. Being that AI is core [7:07] to our business, but we're not just [7:08] purely an AI company. Um, I wouldn't say [7:10] that that we're out there saying this is [7:12] like ethical frameworks for AI, and [7:13] there's a lot of people that are doing [7:15] that. Thank god we're past the temper [7:17] tantrum phase of AI where everyone's [7:18] just sad and scared and weird. Um, and [7:20] now we're in the point where people are [7:21] like, okay, now we it's here. We're in [7:23] the acceptance phase of grief, right? [7:25] And so like we're like actually working [7:26] through it and imple implementing it [7:28] which is awesome. Uh but but for us we [7:30] have to think about kind of like what [7:31] are the what are the results of taking [7:33] the insights from data from the AI [7:36] systems in the data that we get to apply [7:38] to whether it's animal welfare in the [7:39] case of like the direwolves or in cases [7:41] of human genome engineering. And so if [7:43] you look at Colossal, we're making 75 [7:45] plus edits at a time um using kind of [7:48] our system and and and our models and [7:50] that actually help us design these [7:52] guides of how we are delivering large [7:54] payloads into cells and other labs are [7:56] doing too. So that's quite a big step [7:58] function, right? Right. And so so when [8:00] we think about that and we think about [8:01] then the application of those [8:02] technologies to humans uh and you know [8:05] there's now companies wanting to do [8:06] editing in embryos for humans you know [8:08] there are ramifications to the [8:10] advancement of these technologies and AI [8:12] and then eventually quantum are just [8:14] going to massively accelerate that. And [8:16] so on one hand you can say we want to [8:17] cure disease states on the other hand is [8:19] like okay how much germline editing do [8:21] we want to do in the gene pool that can [8:23] be passed on the next generation. And so [8:25] those are like really hard not just [8:28] regulatory issues but moral hazard [8:30] issues. It's like where do we draw the [8:32] line? If we can eradicate a disease in [8:34] in a child do we do it or do we say you [8:36] know if we can you know increase [8:38] intelligence do we do it? So these are [8:40] the things that I think that really [8:42] synthetic biology genome engineering and [8:44] uh AI and eventually quantum like that [8:47] kind of convergence is going to really [8:49] change uh and make us really think about [8:51] what it means to even be human. And when [8:53] you try to communicate to to to people [8:54] outside of the the company or even [8:56] outside of AI. Yeah. Um you had a funny [8:58] post on Twitter recently about how you [9:01] >> I get a lot of feedback from my post. So [9:02] I I make less posts these days. [9:04] >> Yeah. But it was a good post because it [9:06] was saying like you get the same 10 [9:08] questions over and over. You could train [9:09] an LL. [9:10] >> Oh my l [9:10] >> to answer them. I mean [9:11] >> so you know what's interesting I said [9:12] that and then someone made a woolly [9:14] mammoth chatpot. I didn't make that. [9:16] Like someone made a woolly mammoth [9:17] chatpot. They're like okay. And then [9:18] they they made it. Yeah, I I did make a [9:20] joke saying that like we get asked to [9:22] because it's interesting, right? It's [9:23] like we we'll talk about like [9:24] conservation. Like all the technologies [9:26] we make are available for conservation. [9:28] We're deploying technologies into [9:29] conservation. We even raised a [9:31] foundation with $50 million for for [9:33] conservation. And then people will be [9:34] like, "You guys should do stuff for [9:36] conservation." It's like, "Well, there's [9:37] a button on our website says [9:38] conservation. We have a foundation. We [9:40] we mention it every time we open our [9:41] mouths." So I was like, "Maybe I'll just [9:43] make an LLM and like like we'll just [9:45] take down all of our stuff and you can [9:46] just talk to a bot." Well, and I was [9:48] going to say, how crucial is it to be [9:49] communicating with the public right now [9:51] about like how this technology works, [9:53] the safeguards being put around them, [9:55] the thought that is being put into it? [9:56] >> I think it's really important. And so, [9:58] and so one of the things that when I [10:00] started the company with George, I [10:01] thought we would spend a lot of time in [10:02] the lab, maybe some like, you know, [10:04] dungeons and like subbins like the [10:07] Smithsonian, but I and maybe some field [10:09] work, but I never thought that we would [10:10] spend, you know, half or more than half [10:12] our time in the field, right? And so, we [10:15] spend a lot of time educating. We spend [10:16] a lot of time with uh uh lawmakers. We [10:18] spend a lot of time with governments [10:19] because the work that we're doing [10:21] doesn't, you know, it transcends [10:23] borders. It it transcends geopolitical [10:26] uh frameworks that that currently are [10:28] existing, right? You know, like mammoths [10:30] don't see borders. You know, do saw a [10:32] little bit of border because they were [10:33] an island, right? But some of these [10:34] species don't. And so we have to think [10:36] about that. And then we're also spending [10:37] a lot of time uh with even indigenous [10:39] people groups, right? And so we just [10:41] announced our MOA project. And you know, [10:43] before we ever would work on the MOA [10:45] project, we went and worked with the [10:46] Nahu Research Center and the the Maui [10:49] people because that's part of their [10:50] Tonga and their sacred species. [10:52] >> I was going to say, what is the MOA [10:53] project? [10:53] >> So, so the MOA is awesome. Uh it is uh [10:57] you know, I'd argue it's the closest [10:58] thing. It's it's not genetic. I'll be [11:00] careful about saving. It is not [11:02] genetically the closest thing to a [11:03] dinosaur, but it is probably the closest [11:05] thing that humans live next to a [11:06] dinosaur. It's a 12- foot flightless [11:09] bird that didn't have wings. uh that was [11:11] native to to New Zealand. And uh it's [11:13] awesome. It was it was really helpful to [11:15] the uh vegetational overgrowth of of [11:17] canopies. Uh the Mauy people actually [11:19] hunted it to extinction um you know a [11:21] couple about 600 years ago and uh we [11:24] actually have the DNA to bring it back. [11:26] But even in that process, not only [11:27] educating uh lawmakers and and others in [11:31] the field and journalists and the public [11:32] at large, we have to go work with the [11:34] indigenous people groups to make sure [11:36] they understand the application of the [11:38] technologies, where the limits are and [11:39] also get their permission and buy in. So [11:41] we have to spend a lot of time, I'd say [11:44] half of our time is spending on [11:45] education and transparency, which I [11:48] think is really uh important even if I [11:50] occasionally make jokes on Twitter. [11:52] >> Fair enough. Uh the applications of AI I [11:54] think is is what is most interesting [11:57] about what you guys are doing, right? Um [11:59] you know a lot of people here I think [12:01] know that AI is more than just a chat [12:03] GPT or a bot. [12:05] >> Um but talk to me about some of the [12:07] applications you guys are starting to [12:09] pursue on the bleeding edge. What are [12:11] you excited about starting to really [12:13] focus on? So one of the things that that [12:15] we are just now testing uh and it's [12:17] working so far uh but it's still the [12:19] early days is how can we uh create [12:22] feedback loops from so we make a lot of [12:24] cell lines right and we make a lot of [12:26] different edits and we're using [12:27] different combinations edits and [12:28] different edits uh whether you're [12:30] inserting a big piece of DNA or just [12:32] knocking on a small piece or just [12:33] changing one little rung on that you [12:35] know twisted ladder of DNA uh we we like [12:38] different ones have different editing [12:40] modalities and different editing [12:41] modalities have different efficiencies [12:42] and so we're combining all that in in a [12:44] pretty interesting way. Um, but if we [12:46] just do that randomly and and one cell [12:48] line works and one cell line doesn't, [12:49] you know, how do we know if you know [12:51] what we're doing is the right way? And [12:53] so so feeding all this data into a [12:55] feedback loop because we're now creating [12:57] enough data that we can actually train a [12:59] system. So we're now actually have a [13:00] recommendation system that's actually [13:02] saying, okay, for this type of edit, if [13:04] you want to produce this type of result, [13:05] you need to use these tools, which is [13:07] really interesting. and where I think [13:09] it's going. Uh, and this is where, you [13:11] know, I'm hopeful, you know, we hear [13:13] quantum is two years away every two [13:14] years, but I'm very excited about [13:16] quantum and AI because I I think that [13:18] eventually we can do simulated wetwware [13:20] or or lab work uh using AI and [13:23] eventually robotics. And so if we can [13:26] empower AI and quantum and AI into [13:28] eventual robotics, I'm not talking [13:30] about, you know, the Tesla robots like [13:31] the laundry stuff. School, but [13:33] ultimately I think it'd be really [13:34] interesting to say, you know, how can we [13:36] run simulated uh experiments all, you [13:39] know, in a system and then say, okay, [13:41] here's like the 80% probability if you [13:44] make these edits, then have robots do [13:46] that in in there so that then the [13:47] scientists can just do an analysis of [13:49] that final product, right? And so that's [13:51] where it's going and that's where we're [13:53] spending a lot of time at kind of the [13:54] intersection of of AI and robotics and [13:56] then simulation and and hopefully [13:58] getting ready for quantum. [13:59] >> All right. Well, if if you brought up [14:01] quantum a few times, then we're going to [14:02] have to go there. Uh walk me through why [14:05] quantum and AI uh is is something to get [14:08] excited about. What about quantum? [14:10] >> It's in you know the theory, right? And [14:13] people are doing this. There's there's [14:14] great companies out there doing this, [14:15] but the application of being able to run [14:18] so many simultaneous experiments in a [14:20] true simulation, right? You hear about [14:22] like the fact that like we're in [14:23] simulation, some people believe that. [14:25] Um, some people don't. Uh, but but [14:27] ultimately being able to simulate a lab [14:29] environment in a wetware environment. [14:31] And there's many examples of using [14:32] quantum for communication and defense [14:34] and others that are much more [14:35] interesting probably than than what we [14:36] want to use it for. But the ability for [14:38] us to, you know, look at like what is [14:40] the probab pro probabilistic uh success [14:42] rate of these edits using the data that [14:45] we already know is working that we've [14:46] been feeding back into our AI system and [14:48] then being able to simulate all these [14:50] experiments at once so that we can [14:52] simulate all these different gene [14:53] combinations pulling in the literature [14:55] right so that we can look for uh issues [14:57] like you know the genes that could cause [14:58] deafness or blindness from the [14:59] direwolves in in and if you implement [15:01] them in today's wolves those are the [15:03] things that I think are super [15:04] interesting because then you can [15:06] essentially get that data and insight in [15:08] a report saying okay go try these five [15:10] experiments right versus us just being [15:12] in the field of discovery and I really [15:14] think that that empowers us to move from [15:16] the world of scientific discovery to [15:19] scientific engineering like right now [15:20] you know it used to be like 90% [15:22] discovery 10% engineering I'd say it's [15:24] more like 50/50 or 6040 now but I think [15:27] in the future it's going to be like 95% [15:29] engineering you know because of these [15:31] tools and technologies and 5% discovery [15:33] because the discovery is going to happen [15:34] in the system [15:35] >> that's well and and for lack of a better [15:37] term, you just need more horsepower. [15:38] >> Yeah, we just need more horsepower and [15:40] we need concurrent horsepower [15:41] >> and that's what Quantum can offer. [15:43] >> Yeah. For us, it can offer a lot more [15:45] other things like security to other [15:46] people. Yeah. [15:46] >> Sure. For you guys. Um, in terms of, you [15:49] know, what else you got coming down the [15:51] pipe? Is there anything else? Any other [15:52] animals that we can expect in the the [15:54] near future? [15:55] >> Yeah, we surprised the world with the [15:56] woolly mice. We surprised and Chip and [15:59] Dale doing quite well. Uh, and then um [16:01] we surprised the world with with the [16:02] direwolves and you know, it was [16:04] interesting feedback. It was great. And [16:06] then uh uh no, we're very focused on our [16:08] flagship species. There's nothing else [16:09] to announce yet. Um but uh I think that [16:13] we'll show progress before the end of [16:14] the year. Not a dodo, but we'll show [16:16] progress on our aven genomics group uh [16:18] before the end of the year, which we're [16:19] pretty excited about. We've had a couple [16:20] cool breakthroughs there. Um and then uh [16:22] but we're not going to be announcing any [16:23] more species uh this year. [16:25] >> Yeah, fair enough. And [16:26] >> well, only only two this year. [16:28] >> Yeah. And in in 10 years, where are you [16:30] guys hoping to be? Like what does this [16:32] company look like down the line? [16:33] >> Yeah. I I I think in 10 years if if [16:35] everything goes right, we've brought [16:37] back uh all of our flagship species. [16:39] We've successfully worked with [16:40] indigenous people groups, private land [16:42] owners, and governments to put them back [16:43] into their environments. We're testing [16:45] the applications and the imple in the [16:47] and the implications of of of how [16:49] they're affecting the environment [16:50] hopefully in a positive way. And then I [16:52] hope that the technologies that we've [16:53] developed have have expanded beyond just [16:56] conservation where they're not just [16:58] being field tested like with the [17:00] northern white rhino and vikita type [17:02] projects but actually being implemented [17:04] across the board for everyone. So that [17:05] that that would be where I hope we are [17:08] in 10 years and I think that we're [17:09] currently on pace to do it [17:10] >> and and would you say that [17:14] that means the end of extinction? Like [17:16] is that is that is that even in the [17:17] realm? Yeah, I mean I look it is more [17:19] expensive and this is it's a great [17:21] question. It is way more expensive to [17:23] bring back something uh and re-engineer [17:26] and rebuild extinct species for today [17:28] than just preserve today, right? Like I [17:30] I I would hope that we would change, you [17:32] know, people's views. But we are in in a [17:34] world where 50% of biodiversity could be [17:36] lost between now and 2050. That's not [17:39] that far away. And so another issue, [17:41] another uh implication that that we're [17:43] we're doing right now is the [17:44] implementation of uh bio vaults. So [17:47] we're working with several governments [17:48] around the world to actually uh uh [17:50] protect their species uh take their [17:52] samples and and not just put them in a [17:54] fridge like some people are doing in bio [17:56] banking but actually go do full analysis [17:58] on and do uh all the ATAC on them do all [18:00] the different uh genome sequencing build [18:02] a population diversity map so that we [18:04] say okay yeah there may be 200 rhinos [18:06] over here but are they healthy what are [18:08] they inbreeding where where are there [18:10] going to be forecasted genetic [18:12] bottlenecks so so that's a big [18:13] initiative that that we have and our [18:15] foundation has uh to do that so that [18:17] it's almost like a Noah's Arc type [18:19] mission. Um because I I do think, you [18:21] know, it's going to take a long time to [18:22] change it's going to be much harder to [18:24] change consumer behavior uh than to [18:27] advance the technologies. And so um you [18:29] know I still think it's cheaper and [18:31] better to save what we've got than what [18:32] we do. And that's weird coming from the [18:34] guy that runs a deextinction company, [18:35] but I think it's much better to do that [18:36] than what we do. But but I think we need [18:38] a safeguard and and we just don't have [18:40] it because we know that while modern [18:42] conservation works, it's not working at [18:44] the speed at the speed of which were, [18:46] you know, eradicating [18:47] >> Absolutely. Ben, thank you so much. This [18:48] was wonderful. I think we just exactly [18:50] nailed time. [18:50] >> Oh, awesome. Thanks. [18:52] [Applause]