OpenAI CFO Links Compute Spend to Revenue Growth as AI Demand Surges

AI需求激增，OpenAI CFO将算力支出与营收增长直接挂钩 人工智能的快速扩张完全受制于基础设施的供给能力而非市场需求，这为未来15年内由近乎免费的劳动力与专业知识驱动的深度通缩型经济铺平了道路。 在本期对话中，Sarah Friar与Vinod Khosla概述了定义2026年AI格局的趋势——向成熟多智能体系统和垂直专业化转变。他们通过指出真实世界中巨大的生产力提升，以及OpenAI爆发式增长的算力基础设施与营收增长之间的直接关联，驳斥了对AI泡沫的担忧。对话重点探讨了AI对医疗和企业工作流的深远影响，同时为初创公司基于前沿模型进行构建提供了战略路线图。展望更长远的未来，他们预测将出现一个规模达万亿美元的机器人产业，以及由近乎免费的劳动力与数字化专业知识驱动的深度通缩型经济。 Vinod Khosla 将 AI 视作如同电力般的基础设施，强调当前行业需求完全受限于算力供给，多智能体系统预计将在 2026 年成熟并全面接管企业工作流。 (0:00) Sarah Friar 指出当前存在巨大的“能力差距”，只有个位数比例的用户发挥出了现有技术 30% 的能力，普及与深度利用将是一场长达 10 年的学习旅程。 (2:30) OpenAI 的历史数据展现了算力投入与营收增长的高度一致性，其规模已从 2023 年底的 200 MW（对应 20 亿美元 ARR）扩张至 2025 年底的 2 GW（对应超 200 亿美元 ARR）。 (11:51) 针对“AI 泡沫”质疑，两位嘉宾指出应以 API 调用量和实际生产力来衡量行业健康度；目前前四分之一的落地企业生产力提升了 27% 至 33%，更有初创公司仅凭一名会计即实现了 1.5 亿美元 ARR。 (24:11)

OpenAI CFO Sarah Friar and investor Vinod Khosla argue that artificial intelligence represents an infrastructural shift limited by compute capacity rather than a speculative bubble. They contend that closing the gap between baseline adoption and frontier capabilities will transform enterprise workflows, clinical healthcare, and global labor economics. Compute Scaling and the Bubble Debate (11:51) Khosla dismisses claims of an AI bubble, arguing that actual demand is virtually unlimited and should be evaluated by API calls and traffic rather than equity market volatility. Comparing AI to foundational utilities like electricity, the speakers state that market supply is strictly constrained by compute availability. Friar notes that OpenAI closely pairs infrastructure spending with commercial growth, outlining historical and targeted capacity milestones: 200 megawatts supporting $2 billion in Annual Recurring Revenue (ARR) at the end of 2023, 600 megawatts supporting $6 billion at the end of 2024, and 2 gigawatts planned to support over $20 billion in ARR by the end of 2025. To balance operational scale and capital commitments, Friar outlines a three-layer business structure spanning multi-cloud and multi-chip infrastructure, a broader product catalog, and diversified revenue channels encompassing enterprise software-as-a-service, API credit pricing, advertising, and prospective licensing royalties from model-driven discoveries. The Capability Gap and Enterprise Workflows (2:30) Friar and Khosla emphasize that broader market adoption substantially lags behind existing model intelligence. Khosla estimates that only a single-digit percentage of users exploit even 30% of current system capabilities, while OpenAI data indicates frontier enterprise adopters utilize AI at six times the rate of median users. McKinsey data cited by Friar shows top-quartile enterprise adopters achieving 27% to 33% productivity increases across financial metrics. Specific implementations include automated back-office operations at Slash—which scales to $150 million in ARR with a single accounting controller—and OpenAI's internal pipeline that ingests commercial contracts overnight to evaluate revenue recognition terms. While Friar reports that 90% of surveyed corporations use or plan to adopt OpenAI tools, broader agentic automation remains in an early stage. Only 14% of U.S. corporations currently deploy agentic systems, even as select executives report that up to 60% of their production code is written by AI agents. Khosla projects that by 2026, multi-agent architectures will mature to independently execute complex enterprise tasks, including enterprise resource planning (ERP) reconciliations, accruals, and contract monitoring. Clinical Adoption and Regulatory Obstacles in Healthcare (7:02) Healthcare stands as a critical application area, driven by a structural drop in the cost of medical intelligence while aggregate healthcare spending continues to outpace national GDP growth. Friar reports that 230 million individuals ask ChatGPT health-related queries each week, while 66% of U.S. physicians report utilizing the tool in daily clinical workflows to assist with complex diagnostic pattern recognition, such as identifying rare conditions in emergency departments. Patients similarly use model interfaces for dietary management, symptom research, and medical second opinions. However, Khosla argues that the primary impediment to clinical adoption is institutional and legal rather than technological. Frameworks enforced by the Food and Drug Administration (FDA) and bodies like the American Medical Association (AMA) legally prohibit software from formally prescribing medications or rendering official diagnoses, even when models exhibit superior diagnostic precision relative to human medical staff. Startup Positioning Around Data and Governance (41:08) Despite the expansion of frontier foundation models, Friar and Khosla maintain that startups have significant defensive opportunities by embedding base models within specialized enterprise workflows. With an estimated 95% of corporate and institutional information remaining behind private firewalls, startups can build proprietary value around data permissioning, orchestration, and compliance. As an illustration, automated corporate procurement requires navigating governance rules and human resource directories to verify dynamic signature authorizations, a problem off-the-shelf general models cannot solve natively. Additionally, the speakers anticipate an emerging commercial layer focused on identity, security, and transaction protocols for autonomous agent-to-agent negotiations. When assessing early-stage founders tackling these spaces, Khosla and Friar emphasize adaptability and personal agency over traditional sector experience. Embodied Robotics and Long-Term Labor Deflation (47:01) Looking toward physical systems, Khosla reiterates his forecast that embodied robotics will become a larger commercial industry than automotive manufacturing within 15 years. While initial industrial efforts center on manufacturing floors, the speakers expect the first broad consumer entry point to be companionship for an aging population, prior to the widespread automation of domestic manual labor. By the end of the next decade, the speakers project that near-free intelligence and physical labor will create a heavily deflationary macroeconomic environment, potentially lowering primary medical care to nominal costs and offering universal personalized tutoring. However, the speakers caution that this transition leaves critical distribution issues unanswered, particularly how economic systems will support displaced human labor and manage basic needs like food and housing, which currently consume more than 40% of income for the lower half of the U.S. population. The speakers outline a future driven by exponential compute scaling and multi-agent automation, yet the broader realization of this productivity boom remains bounded by regulatory resistance in critical sectors, infrastructure availability, and the societal challenge of supporting human livelihoods amid extreme labor deflation.

Khosla Ventures 创始人 Vinod Khosla 与 OpenAI 首席财务官 Sarah Friar 指出，当前人工智能并非处于投机泡沫，而是受制于算力供给的基础设施革命。随着技术由浅层单点问答向复杂多智能体协同跃迁，AI 将在深度重塑企业工作流的同时，于未来十年依托边际成本趋零的智能与具身机器人催生深远的通缩型经济。 算力供给约束与反驳泡沫论 (11:51) Khosla 将人工智能定义为类似电力的底层基础设施，而非消费时长存在天然上限的媒体服务。他强调当前市场对 AI 的需求几乎无限，唯一真实的瓶颈是算力供给能力。针对资本市场的“泡沫论”，Khosla 明确予以驳斥，指出公开市场的股价和估值起伏主要由投资者的贪婪与恐惧主导，衡量 AI 生态真实健康度的核心指标在于 API 调用量与底层网络流量；当前基础设施投资是在紧随现有需求，行业甚至尚未启用价格弹性杠杆。 Friar 披露的数据展示了算力投入与商业回报之间的强绑定关系：OpenAI 的算力规模与年经常性收入（ARR）呈现出高度同步的增长趋势，算力与 ARR 分别从 2023 年底的 200 兆瓦对应 20 亿美元，提升至 2024 年底的 600 兆瓦对应 60 亿美元，并预计在 2025 年底达到 2 吉瓦（2000 兆瓦）对应超过 200 亿美元。为支撑这一扩张，OpenAI 正在推行多云、多芯片的底层架构，并在商业化层面从单一的 20 美元订阅转向包含企业级 SaaS、高价值 API 积分制以及药物研发分成等多元模式；针对占消费用户 95% 的免费群体，未来引入广告也将严守不干扰答案客观性、提供实用交互与保留无广告付费版的三大原则。 弥合能力鸿沟与多智能体工作流重塑 (2:30) Friar 与 Khosla 均强调，当前产业正面临巨大的“采用曲线与能力曲线的脱节”。Khosla 估计目前仅有个位数百份比的用户利用了 AI 技术 30% 以上的实际潜能，人机协作仍处于长达十年的认知演化初期。尽管 ChatGPT 已累积超 8 亿周活跃消费用户，但多数仍将其作为基础问答工具；在企业端，OpenAI 统计的前沿公司 AI 使用频率已达中位企业的 6 倍，但仍未触及技术上限。随着 2025 年提示词编程的成熟，两位受访者预测 2026 年多智能体系统将全面落地，承担起端到端的 ERP 运维、合同核对乃至跨平台多步骤行程规划等重度任务。 这种演进正在微观组织中释放剧烈的效率提升。Friar 引用麦肯锡数据指出，采用 AI 的头部四分位企业在财务指标上取得了 27% 至 33% 的生产力飞跃，例如 OpenAI 内部已利用多智能体流水线在夜间全自动完成日常销售合同审查与收入确认。Khosla 举例称，金融科技公司 Slash 仅凭一名财务主管便支撑起 1.5 亿美元的 ARR，另有企业以“1 名员工加 AI 智能体”替代了原先 10 人的销售开发代表团队。然而，全美仅约 14% 的受访企业当前应用了智能体技术，显现出巨大的扩散与增长空间。 医疗专业知识普惠与专有数据壁垒 (7:02) 在医疗领域，AI 正在推动高价值专业知识的普惠化。Friar 披露，每周有 2.3 亿人通过 ChatGPT 咨询健康议题，全美已有 66% 的医师在日常诊疗工作中辅助使用该工具以降低罕见病误诊率。Mayne 指出，在各国医疗保健支出增速普遍超越 GDP 增长的背景下，AI 带来了医疗智能成本的历史性同比下降。但 Khosla 强调，医疗落地的主要障碍在于制度与监管层面，FDA 以及美国医学会（AMA）等行业组织在法规上严格限制 AI 独立开具处方或进行法定诊断，形成技术能力与应用许可之间的张力。 面对科技巨头的基础模型演进，初创企业的生存空间成为焦点。讨论指出，全球约 95% 的信息仍留存于企业及机构防火墙之内，初创公司的核心壁垒在于整合专有数据并驾驭复杂的特定业务流程。以企业采购为例，系统必须动态协同董事会审批限额与人事组织架构的权限控制，通用大模型无法开箱即用解决此类复杂的企业治理逻辑。随着智能体自主交易与数据权限管理需求的涌现，聚焦工作流集成的初创团队仍拥有广阔的发展空间。 具身智能演进与长期通缩经济展望 (44:05) 在物理世界维度，Khosla 维持其长期预测，认为包括双足机器人在内的具身智能产业将在未来 15 年内发展成规模超越当今汽车工业的独立经济支柱。尽管技术演示常聚焦于叠衣服或煮咖啡等家务活动，但讨论指出，面对全球老龄化与孤独感加剧的趋势，能够提供情感共情与语言陪伴的人形机器人很可能是率先打开消费级市场的突破口。 展望下一个十年末，对谈预测劳动力与专业知识成本的大幅下降将催生“深度通缩型经济体”。届时高质量的基础医疗护理可能降至每月 1 美元，每位儿童都能享有近乎免费的专属 AI 辅导，从而显著提升全球最低生活标准。然而，如何构建新的收入分配机制将成为政府的核心课题，且在美国家庭收入后 50% 人群中占据逾 40% 支出的食品与住房等实体刚性需求，其降本难度仍远高于数字化智能，有待先进机器人与供给侧技术取得协同突破。 本期对话确立了 AI 从单一工具向全行业环境基础设施演进的长期框架，但核心挑战依然在于现实摩擦力：这包括医疗等高门槛领域的制度性准入壁垒、人类组织适应新技术能力的学习周期，以及如何在算力与商业化扩张过程中维系用户的隐私与信任。

[0:00] Vinod Khosla: vibe coding [2:30] Sarah Friar: the keys to the Ferrari [5:28] Sarah Friar: It's like we've just turned electricity on in the home... they've turned on the lights, but they have no idea that they could now heat their home. [17:09] Vinod Khosla: have no sense of how large this change is [24:11] Vinod Khosla: the future is here now, but it's not evenly distributed [27:37] Sarah Friar: AGI for the benefit of humanity, not the benefit of humanity who can pay [27:37] Sarah Friar: North Star [32:40] Sarah Friar: talk with our thumbs [36:33] Sarah Friar: tidal wave of consumer preference [39:07] Vinod Khosla: didn't even know what production code meant 12 months ago [39:46] Vinod Khosla: no one company can do everything on the planet [39:46] Vinod Khosla: intelligence isn't the only thing to provide a solution [41:08] delegation of authority [42:36] it's never been probably more interesting or fun to be a startup [47:01] massively deflationary economy

[0:00] Hello, I'm Andrew Mayne, and this is the OpenAI Podcast. Today, our guests are Sarah Friar, [0:06] CFO of OpenAI, and legendary investor Vinod Khosla of Khosla Ventures. In this discussion, [0:11] we're going to talk about the state of the AI ecosystem, whether or not we're in a bubble, [0:16] and how startups and investors can succeed as AI progresses. [0:21] Unlike something like Netflix, where they're running so many hours in the day, [0:25] I think of it much more like infrastructure, like electricity. [0:27] Demand is limited, not by anything other than availability of compute today. [0:33] I think the conversation we need to have is, what will people do? [0:41] 2025 was about agents and vibe coding. [0:44] Now it's 2026. [0:46] What's the story of 2026? [0:47] I think we matured in vibe coding in 2025. [0:52] I don't think we've matured in agents. [0:54] So agents, especially multi-agent systems, will mature to the point of having real visible impact. [1:03] Whether you're an enterprise and you have multi-agent systems doing full tasks, [1:09] like running an ERP system for you, doing all the reconciliation every day, accruals every day, [1:18] tracking contracts every day. [1:20] I think that on the enterprise side. [1:24] But today, on the consumer side, you know, it's still a hassle to plan a trip. [1:31] That's a multi-agentic thing that looks across a lot of different things from your food preferences to the restaurant reservation to airline schedules to your personal calendar. [1:44] Those will start to mature, I think, a year from now. [1:49] So I'm pretty excited about that. [1:51] I think models in robotics and real world models [1:54] that go well beyond robotics, [1:57] like general intuition, [2:00] will all start to happen in the next year. [2:04] So I think those are areas to look for. [2:06] There's usual functions like memory in LLMs, [2:12] continual learning in LLMs, [2:17] reduction of the impact of hallucinations. [2:20] Those are all areas I could go on. [2:22] There's half a dozen areas in which AI doesn't do as well today that will start to be addressed. [2:29] Yeah. [2:30] And I think at its baseline, what Vinod is saying is 26 is the beginning of closing this capability gap. [2:37] So what we know is we've handed people massive intelligence, right? [2:41] We've handed them the keys to the Ferrari, but they are only learning how to take it out on the road for the first time. [2:48] We need to give consumers more and more easy ways to go from ChatGPT is just a chat bot call and response. [2:58] Most people use it today just to ask questions. [3:01] But how do we take it towards being a true task worker that books that trip for them or helps them get a second opinion on what they just heard from their doctor or enables them to create a menu for their diabetic child, right? [3:18] How do we help them really move from simple questions into actual outcomes that make my life better? [3:26] And then on the enterprise side, it's that same continuum. [3:29] How do we close the capability gap? [3:31] One of the things we know from our state of the enterprise AI and the enterprise report that our chief economist put out at the end of last year is on the frontier versus just even the median corporation. [3:43] The average number of messages or the median is about 6x, which will tell you that 6x the usage from a company that's already on the frontier. [3:52] And we know that frontier isn't even pushed to its max. [3:56] So for us, it's this focus of how do we help consumers move along that continuum to true agentic task working? [4:04] And then for enterprises, how do we create a much more sophisticated, vertically specialized outcome for enterprises that allows them to go from maybe a very simple ChatGPT implementation the whole way to something that's transforming the most important part of their business. [4:21] For a healthcare provider, it might be their drug discovery process. [4:25] For a hospital, it might be the time to admit a patient to get that patient back into the community. [4:30] For a really large retailer, it might be just larger basket sizes, higher conversion rates, and much happier customers. [4:39] So it's the basics of closing that capability gap. [4:43] So I might add one other perspective. [4:45] We've talked about the number of areas in which the technology will advance and capability will advance. [4:51] I would venture to guess today, of the people using AI, whether it's personal or enterprise, [4:59] some single digit percentage are even using 30% of the capability of the AI. [5:05] So this percentage of people who are using 30% or 50%, let alone 80% of the AI's capabilities, [5:13] will keep increasing. [5:15] I think that's a 10-year journey before people learn to use AI. [5:19] I've seen this, some people kind of pundits confuse adoption curves for capability curves. [5:25] And that's come up where you've seen people- [5:27] So that's the point I'm making. [5:28] And it's a force multiplier because today we have over 800 million using, [5:32] ChatGPT today, 800 million consumers weekly using. [5:36] But that number should be in the billions. [5:39] And then what percentage use are they using it for? [5:42] It's like we've just turned electricity on in the home. [5:45] We've wired up the home and they've turned on the lights, [5:47] but they have no idea that they could now heat their home. [5:50] They could cook, they could curl their hair, right? [5:52] There's so many things you now can do. [5:54] An analogy I've used is that email didn't really get much better [5:58] between 1990 and the year 2000. [6:00] Neither did mobile, but usage went way up. [6:01] And the problem wasn't like, well, we need better email. [6:04] We need more better mobile. [6:04] It's like people just need to learn all the things they could use it for. [6:07] Right, yeah. [6:09] And in a more sophisticated way, [6:10] like mobile is always one that's interesting to me [6:12] because when mobile took off, [6:13] people just took their desktop websites and turned them into mobile. [6:16] And they were really hard to scroll, but I guess you at least had them in your pocket. [6:19] But then you realized you had a GPS. [6:22] So now you could have Uber and now you could do things with location or you had a camera at your fingertips. [6:27] OK, so now, yeah, I can take photographs of all my friends, but I can also snap, you know, a check and deposit it into my bank account. [6:34] Although we should fix the whole paper check thing. [6:36] But that's an aside. [6:37] It still seems like I can just take a photo of this and now I get money in my bank account. [6:42] Yeah. But, you know, that all existed in the minute mobile was available to us. [6:48] But just the ability for human ingenuity to come to work on it. [6:52] So I think you're right. [6:53] I don't even know if we need more intelligence than we have today to vastly increase outcomes. [6:59] But, of course, the models are going to keep getting more intelligent as well. [7:02] You mentioned health, and that's one of the really kind of high stakes things we think about when it comes to just probably the most important thing. [7:09] And it's kind of fascinating to think about that just, you know, a few years ago, we got [7:14] ChatGPT and we're using it for very simple applications. [7:16] And now we're trusting with HIPAA compliant data. [7:19] Do you look at that as sort of a marker of how fast or how well things have been accelerating? [7:23] Are there other ones like that you think about to say, OK, now we know we're some new level? [7:27] Health is clearly one of those areas I've long believed will revolutionize health by [7:34] making expertise be a commodity. [7:38] in all areas of health. [7:41] The problem with health is regulatory. [7:47] So first, there's constraints on what AI can do. [7:52] And AI can't legally write a prescription, [7:56] even if it's better than human beings at writing a prescription. [8:01] That is not only the FDA, [8:02] but it's actually beyond the FDA [8:05] into the American Medical Association, [8:08] institutionally controls that function. [8:11] So they will be incumbent resistance in a lot of areas. [8:15] I think we can talk about it if you like. [8:19] But diagnosing is still a constraint [8:23] because the FDA controls that. [8:26] There's no AI approved as a medical device yet. [8:30] So that all, fortunately, [8:32] this administration is doing a very good job [8:35] of moving quickly and taking the appropriate level of risk. [8:39] So I'm pretty pleased to see what's happening there. [8:43] On the health front, we see in our data 230 million people every week ask ChatGPT a health question. [8:51] 66% of U.S. physicians say they use ChatGPT in their daily work. [8:58] I'll tell you at a personal level, my brother is an HDU doctor in the U.K. [9:02] So his job is, right, you hit the ER, they don't know how to triage you, so they send you to him. [9:07] You kind of don't want to show up to him. [9:09] He's expected to have. [9:10] He's very good, though. [9:11] He's very good at what he does, but it means you're not in good shape. [9:14] But he's expected to have almost an encyclopedic knowledge of every disease that ever existed. [9:20] So I always give the example, he works in Aberdeen in Scotland. [9:23] If you showed up with malaria, he will not think of that. [9:26] That is not in his pattern recognition. [9:28] And yet, that could have happened. [9:29] I don't know. [9:29] You went on vacation somewhere. [9:31] You got bitten by a mosquito. [9:32] boom, you're showing up in an ER room in Aberdeen. What ChatGPT can do or what the model can do [9:39] is really act as a great augmentation to the doctor, which is why I think 66% of them are [9:45] using it. And that number is only growing, right? You know, it's probably already much higher. [9:52] And so I think it's just a great example of where something like health, we're getting the benefit [9:57] of our doctors being able to have always the latest research in front of them, always the [10:02] known interactions, say, between someone's drug regime and what they're living through and [10:07] experiencing as individuals. But it also puts some independence back into consumers' hands. [10:14] So now I get the opportunity to, ahead of time, do some research on what my symptoms might be [10:20] saying so I can have a much more educated conversation with my doctor. It allows me to [10:25] maybe get a second opinion or know that I want to go ask for a second opinion. [10:30] It also, we go very fast to, you know, these extreme places. [10:35] But just even things like, hey, I've got 20 minutes a day to exercise. [10:39] I know I'm suffering from type 1 diabetes. [10:42] What could I do in 20 minutes? [10:44] Or my daughter has an interesting issue with the food she eats. [10:49] And so it used to be a super just frustrating thing to go to a restaurant even because we'd have to almost ask the server so many questions. [10:58] And now we can photograph a menu, chat suggests what are likely the best dishes for her to order. [11:04] And then we can have a bit more of a terser conversation, but a bit more productive on what's going to work. [11:10] And it has just changed how we think about just eating. [11:13] Takes it away from all about the food to why we're going out for dinner together. [11:18] And so I think there are all these just examples of something like health. [11:22] It's already happening and it's going to keep getting better and better. [11:26] And then to Vinod's point, I think regulatory environment is going to have to catch up. [11:31] It's no matter what kind of system you're under, the cost of medical care is exceeding the GDP of every country, the rate at which increases. [11:39] And it seems like we needed AI. [11:40] We needed it now. [11:42] And, you know, it can be helpful. [11:43] And as you pointed out, it's the first time the cost of medical intelligence has dropped year over year. [11:49] But that comes with a lot of demand for compute. [11:51] And we have a lot more questions, you know, that we want to have answered. [11:54] And certainly people can see the need for more compute, but the scale and scope at which OpenAI is investing in compute is incredibly huge. [12:05] You know, we're talking, you know, numbers that are just really hard to fathom. [12:10] How does OpenAI determine that need? [12:13] You know, what are the metrics you're looking at to think that, like, yes, we need to spend this much? [12:16] So, first of all, we are trying to make sure we stay investing in compute to match the pace of our revenue. [12:23] And we've seen a really strong correlation between in-period compute and in-period revenue. [12:29] I'll give you an example. [12:30] If you just go back in 23, 24, and 25, our compute was 200 megawatts, 600 megawatts, [12:38] when it ended last year at 2 gigawatts. [12:41] Against that, and it's really easy because the numbers match up, we exited 23 at 2 billion in ARR. [12:47] So 200 megawatts, 2 billion. [12:49] We exited 24 at 6 billion. [12:51] So 6 billion, 600 megawatts. [12:52] megawatts. And we exited last year a little over 20 billion, 20 billion, two gigawatts. Actually, [12:58] it's been accelerating. So that's just even if you look at the slope of the line, it says [13:03] more compute, more revenue. Now, there is definitely a timing mismatch because I have [13:09] to make decisions today about making sure we have compute in not even 26 or 27, but 28, 29 and 30. [13:17] Because if I don't put in orders today and don't give the signal to create data centers, it won't be there, right? [13:24] Today, we feel absolutely constrained on compute. [13:28] There are many more products that we could launch, many more models that we would train, many more multimodality things we would explore if we had more compute today. [13:38] So, for example, even in the last year, I think the overall hardware investments globally has gone up by something like $220 billion. [13:47] That's just how much actual spending has gone up. [13:50] If you look at chips, chip forecasts have gone up similarly about $334 billion. [13:57] So it's not just OpenAI. [13:59] The signal from the whole environment is AI is real. [14:03] We are in a paradigm shift. [14:05] We need to invest to give people the intelligence they need to do all the things we just talked about, for example. [14:12] So back inside of OpenAI, we do spend a lot of time going very deep on what is our demand signal in consumer, in enterprise, in developers. [14:23] We think about what's the mosaic first at the base, like on an infrastructure layer, how do we create max optionality? [14:31] So we want to be multi-cloud, multi-chip, and that gives us an interesting layer at the infrastructure layer. [14:39] One tick up at the product layer, we also want to become more multidimensional. [14:43] So we used to just be one product, ChatGPT. [14:47] Today we are ChatGPT for consumer with all of the blades inside it, healthcare and so on. [14:53] ChatGPT for work, but we also have Sora as a new platform. [14:58] We have some of our transformational research projects. [15:02] One tick up, we also then have a business model ecosystem that's becoming much more multidimensional. [15:10] Began with a single subscription because we'd launched ChatGPT and we needed a way to pay for the compute. [15:15] We now have multiple price points. [15:17] First ChatGPT subscriber, by the way. [15:19] I love you for that. [15:21] Multiple subscriptions. [15:22] We went to the enterprise and had SaaS-based pricing. [15:25] We have credit-based pricing now for places where high value is being found. [15:30] People want to pay more to get more. [15:33] We're beginning to think about things like commerce and ads. [15:36] And then, of course, longer term, I like models like, for example, would we do licensing models to really align? [15:44] Let's say in drug discovery, if we licensed our technology, you have a breakthrough. [15:48] That drug takes off and we get a licensed portion of all its sales. [15:53] It's great alignment for us with our customer. [15:56] So if you think about those three tiers, I actually think of it like a Rubik's Cube. [16:01] So we went from a single block, one CSP, Microsoft, one chip, one product, one business model, to now a whole three-dimensional cube. [16:12] And one of the things I love about a Rubik's Cube, I'm probably not getting the number exactly right, but I think it has 43 quintillion different states it can be in. [16:20] It always blew my mind when I was in university. [16:23] So now just think about that cube spinning. [16:26] So we pick a low latency chip going alongside something like coding that's 5x the pace that people expect. [16:34] We can charge a high end subscription for that. [16:37] So it's almost like you line up the cube and you get three colors on one side. [16:43] We could spin the cube again and say low latency chip, faster image gen, more free users come in. [16:50] But that creates more inventory for ultimately perhaps an ads platform. [16:54] So you can start to see how the goal in the last 12 months has been creating more and more strategic options that allow me to keep paying for the compute we need to really achieve our mission, AGI for the benefit of humanity. [17:09] So, you know, the way to simplify that, demand is limited not by anything other than availability of compute today. [17:19] Whether it's Sora or more broadly. [17:21] And then there's price elasticity, where demand is infinite for compute. [17:29] So I think that's the way to think about it. [17:33] We haven't even started to exercise the price elasticity lever. [17:39] It's just we can't fulfill demand. [17:41] And it's limited by compute. [17:43] So all the people talking about bubbles and things, I think, are on the wrong track. [17:50] They have no sense of how large this change is and how much more demand elasticity there's a need for API calls. [18:02] As one of OpenAI's earliest investors, you made a bet early on. [18:07] You saw where this was headed, but you saw the dot-com bubble. [18:12] You watched what happened there, but you've also seen other things, the mobile revolution. [18:15] You've seen this happen with other areas. [18:18] And you mentioned the term broad. [18:20] And is that sort of where your conviction comes from, is just how many different areas it touches? [18:24] Look, when we invested, we had one simple metric. [18:28] There was no projections to look at, no product plans to look at, no Chat GPT to look at. [18:35] It was very simply the idea. [18:38] If we develop anywhere near close to human intelligence, let alone supersede human intelligence, its impact is going to be huge. [18:49] So it was this hand-wavy approach, like the consequences of success are really going to be consequential. [18:57] So why not try that? [19:03] There's also this funny notion of bubble. [19:06] people equate bubble to stock prices, [19:09] which has nothing to do with anything other than fear and greed among investors. [19:14] So I always look at bubbles should be measured by the number of API calls. [19:22] Or in the dot-com bubble, which people refer to, [19:25] it should be amount of internet traffic, [19:28] not by what happened to stock prices [19:31] because somebody got overexcited or underexcited [19:34] and in one day they can go from loving NVIDIA to hating NVIDIA because it's overvalued. [19:42] Those gyrations aren't reality. [19:45] The reality is the underlying number of API calls. [19:49] If you look at internet traffic during the dot-com bubble, [19:54] prices may have gone up violently and gone down violently. [19:59] There's no bubble detected in internet traffic. [20:04] I would almost guarantee you, you won't see the bubble in number of API calls. [20:12] And if that's your fundamental metric of what's the real use of your AI, usefulness of AI, demand for AI, you're not going to see a bubble in API calls. [20:25] What Wall Street tends to do with it, I don't really care. [20:28] I think it's mostly irrelevant. [20:30] Great for press articles because press has to fill their column. [20:34] inches, but it's not reality. So prices of things aren't reality or stock prices, [20:42] private company valuation. The reality is what's the actual demand for AI, which is the number of API calls. [20:50] Right. And I think if I hark back to that moment where you were looking at 1999, [20:57] The value people were getting from the internet at the time was actually very, it was so young, so nascent that you couldn't really see how it was changing their lives. [21:08] I do think that with AI, it's happened so fast, that change. [21:13] It's very real. [21:14] Like as a CFO, forget about being the CFO of OpenAI, but as a CFO, what I see happening in my organization is truly taking tasks that previously I would have kept having to add more and more people doing fairly mundane things. [21:30] Like let's take something like revenue management. [21:33] So in a team that does revenue management, one of the things they do every day is they have to download all the contracts that we signed the day before or through the week. [21:44] And they have to read all of those contracts to make sure there's no terms sitting in it that are unexpected, that are effectively non-standard terms. [21:53] Because a non-standard term means that there could be a revenue recognition change that has to happen. [21:59] And that's a very big deal for a finance team. [22:01] That's the number one thing usually your auditors come in to audit you on. [22:05] The pace at which we are growing, right, that number of contracts every day is going up in multiples. [22:12] So my only choice in a pre-AI world would have been hire more people. [22:18] And imagine what those people's jobs are like. [22:20] You come to work every day and you read a contract and then you read the next one and the next one. [22:24] It is so mundane and such drudgery. [22:28] And it's not why people went to school and learned about the accounting field or thought about being a finance professional. [22:36] But that's kind of the job we hand them as an entry-level job. [22:40] Today, using our own tools here at OpenAI, I now have overnight, all of those contracts are pulled out of a system. [22:47] They are put into a tabular database, the Databricks database in our case. [22:53] The agent or the intelligence is able to go through. [22:56] It shows me exactly what is non-standard and why. [22:59] It suggests what, therefore, the rev-rec is. [23:03] But it also suggests the insight, which is, you know, should this term even be here? [23:08] Did the salesperson just give away something they shouldn't have? [23:11] In which case, you know, I go and I coach them. [23:15] Is it actually telling me something about my business that's starting to shift? [23:18] In which case, this non-standard term actually should become a standard term. [23:23] And I'm actually, what I'm experiencing is a shift in my business model. [23:26] which might actually be a good thing, or perhaps I want to find a different way to help get the [23:34] customer what they're looking for, the salesperson what they're looking for, but maintain my revenue [23:39] recognition, my current business model, right? So I know my more junior entry-level people [23:45] are over on the right of that discussion, and they're kind of refinding the job they loved. [23:51] That to me is why it's not a bubble, because the value is real and tangible. [23:57] It also means I probably can have a smaller team. [24:00] I can have a much more high performing team, a much higher morale on my team, better retention rates. [24:06] All of these I can put into numbers to say my business is now healthier. [24:11] And I think that's the piece when the press is trying to lead with the bubble conversation or whatever. [24:16] They just miss that we are investing with demand, if anything, behind demand at the moment. [24:22] A bubble to me suggests you're investing ahead of demand and there's going to be a gap. [24:27] And you look at productivity numbers, they're going up in the companies that are adapting AI, especially the newer set of tech oriented companies. [24:37] The numbers are just absolutely amazing. [24:40] So one of my favorites is a little company called Slash. [24:44] About 150 million ARR. [24:48] They have one person in accounting, only a controller, because they adapted an AI-oriented ERP system. [24:58] They replaced NetSuite with it. [25:01] But it's just amazing what they can do. [25:03] And the CEO was apologizing to me. [25:06] He might have to hire a second person. [25:09] And they're moving really rapidly. [25:11] I just saw a story. [25:13] somebody replaced 10 SDRs with one SDR in AI, [25:17] essentially that the one SDR remaining supervises. [25:22] I've been hearing two stories about where instead of hiring somebody [25:26] that's in an area that doesn't create growth, [25:28] they can now then, when they hire, [25:30] hire people that are creating a lot more growth for the company. [25:33] And that's why you're seeing a lot of these tech companies just build so fast. [25:36] You know that old phrase, the future is here now, [25:40] but it's not evenly distributed. [25:42] Yes. [25:43] I see all these single points of huge productivity gains and efficiency gains or agility gains, the ability to move faster. [25:54] But very small percentage of the people in the world or in the U.S. or worldwide have adapted these or even know they exist. [26:04] And so this issue back to demand, I think these ideas, some of these examples will spread to everybody over time. [26:17] And you'll see an exponential growth of adoption of these technologies. [26:21] That's why I don't think demand is the question. [26:24] Yeah. [26:24] Vinod is absolutely spot on. [26:26] I think McKinsey did a study that showed for companies that are more in the top quartile, their productivity as measured by any kind of financial metric you would pull is up in the 27 to 33 percent. [26:38] Like that's a really meaningful jump. [26:41] I think where you were going is it doesn't just mean fewer employees overall. [26:45] There's definitely a place to kind of shift people over into more growth oriented jobs. [26:50] I was hiking this weekend with someone who runs a very large consulting company that you all would know of. [26:57] And he was talking about how his and his what he thinks of more his back end systems. [27:02] The leader there is now talking about her organization as people plus agents. [27:07] And she has a one to five ratio, one person to five agents. [27:11] But on the front end, they're actually back out rehiring to grow because clients need more help now to think about deploying AI. [27:19] So it's actually shifting back, I would say, to the jobs people want to do, not the jobs that maybe were just open to them because more and more of the world had become this kind of, you know, so much information that people were parsing it. [27:33] Now we're finally back to a machine and agent intelligence parsing it. [27:37] I want to touch back on the consumer side. [27:39] You mentioned ads. [27:40] And certainly the argument we made that with ads, you can increase the benefits to people. [27:46] You can provide more services, more AI. [27:48] You can help pay for the compute and people get more out of those tiers with that. [27:51] But that brings up the question, though, of trust. [27:53] And when people think about AI initially even asking questions, people worried about what does ChatGPT do with my information? [28:00] Once you have ads in play, people worry about that because it's often just a big question of how does that affect the rest of the product and the org? [28:08] Yeah. So I think you started in the right place, which is today 95 percent of our users use our platform for free on the consumer side. [28:16] And that's absolutely where our mission is, right? [28:19] AGI for the benefit of humanity, not the benefit of humanity who can pay, right? [28:23] So access is very important. [28:25] From an ads perspective, I think, number one, we have to just make sure everyone understands you're always going to get the best answer the model can provide you, not the paid for answer. [28:37] And I think other platforms have fallen back into that where you're not sure is this a sponsored link or is this truly the best outcome? [28:44] We have a North Star, which is that the model will always give you the best answer. [28:50] I think the second thing to understand is that there can be a lot of utility in ads. [28:55] So we want to make sure people know when it is an ad that they're working with. [28:59] But for example, if I do a search for a weekend getaway to pick your favorite city, I don't know, San Diego, [29:07] an ad for Airbnb might actually be very helpful. [29:10] And you might even want to have a discussion with the ad or with the advertiser in that case in a ChatGPT setting that's very rich, but you're clear that it's in an advertising setting. [29:21] And I think this is where there has to be more innovation on what feels endemic to the platform, not just kind of the old world of stick banner ads on things. [29:33] And I think the third and final thing for me is, again, there always has to be a tier where advertising doesn't exist. [29:39] So we give the user some choice and some control. [29:43] But we're very mindful of your data. [29:45] When we released Health, we were very clear your data is off to one side. [29:49] It's not being used to train on and so on. [29:51] And I think we just need to keep giving users that kind of that trust is everything for OpenAI and that we're going to stand by those principles, even when it comes to things like ads. [30:01] On the consumer side, is it going to be a world where you're going to have a lot of subscriptions to different AI services? [30:07] I think you'll have every model. [30:09] Most people will have more than one subscription. [30:12] Media is a good example. [30:13] Most people have more than one subscription media. [30:17] And so that's a good proxy for consumer behavior. [30:21] Different people will pick different choices, including free choices, which is ad-supported media too. [30:30] So even the same services you can get for pay or for free. [30:35] I think you'll see a wide range of diversity. [30:38] How do you think about, though, the expense of going to a different platform? [30:43] So I like ChatGPT memory. [30:46] I'm finding it more and more helpful because as I ask about one thing, it remembers something we talked about maybe weeks ago, months ago. [30:53] Pulse, which is today not widely distributed, but it's the way I wake up in the morning now. [30:59] It's amazing. [30:59] It's so amazing. [31:00] And when you start connecting it to things like your calendar, so it's not just saying, you know, you say are very interested in AI data centers, which clearly it must think I'm the most boring person on earth because this is what I see a lot of. [31:11] But it also says, hey, on your calendar, you're going to be sitting down with Vinod today. [31:16] You know, remember a couple of these things like it's so helpful. [31:20] But if I am multi homing, I'm losing the benefit, which is not the same as if I subscribe to the Wall Street Journal, The Economist and The New York Times. [31:29] They're not really losing out if I go read in other places in the same way or I'm not losing out. [31:35] Yeah. So I do think memory is an important question, whether there'll be one per wear or more than one per wear of the models. [31:44] On each model, there'll be multiple services that may offer different tradeoffs. [31:49] So even whether you're talking health or media, even on the OpenAI models, there's multiple people providing services. [32:02] So that's what I was thinking of multi-homing. [32:06] But obviously, I don't think OpenAI will be 100% of the market. [32:10] I hope so. [32:11] I was going to say, I hope so too. [32:13] I'm okay with that. [32:14] It's an interesting business model. [32:16] I think it's hard for people to wrap their heads around because like Netflix is a great company, but there's only so many hours on the planet that people can watch Netflix. [32:24] Right. And mobile is great. Right. I only I only need so many minutes of mobile per week or whatever to do that with AI and intelligence. [32:32] You can have more intelligence. I can buy more and get better answers and do this. [32:35] And I think that's I think I'm still trying to wrap my head around about where where that goes. [32:40] The idea that like you start at like, you know, one level of free, you know, use it for free. [32:43] Then you go to a smaller tier. [32:45] And then as it becomes more useful, you start increasing that. [32:48] Where does it go? [32:49] So I think unlike something like Netflix, where they're running so many hours in the day, I think of it much more like infrastructure, like electricity. [32:56] How much electricity do you use in the day? [32:57] I don't know. [32:58] I walked into a room today and there was a fan blowing. [33:00] It was really nice. [33:01] It cooled it down. [33:02] There are lights on around us right now. [33:05] There's so many. [33:06] I charged my phone overnight and it worked for me all day. [33:09] So I think that the state we live in today is much more I call on ChatGPT, I invoke it, as opposed to intelligence just being baked in. [33:20] Like I think this will be the big change over the next couple of years. [33:24] You'll kind of look back almost it'll feel a little toy like that we used to do this thing. [33:29] And instead it just is everywhere around us. [33:32] And so it's not really quite answering the question you're asking, but it's that I don't get so caught up that there's only so many hours for people to do things. [33:42] Because I feel like almost everything I do in life requires intelligence because I'm walking around, hopefully with some intelligence up here. [33:49] And if I can get that augmented, I think it's going to surprise us. [33:54] As we were talking before we got started, you said about on your phone when you suddenly discovered you had a flashlight and a camera. [34:01] It is, you say that, it's so obvious. [34:05] And yet with ChatGPT, every time I discover kind of a, what feels like almost a slightly [34:10] cute use case, I'm so blown away by it. [34:13] Like yesterday morning, I do love The Economist. [34:17] I wanted to read the editorial. [34:19] I didn't really have a ton of time because I was running upstairs to get ready. [34:22] So I took a photograph of the editorial because they're very good. [34:24] They put it on one page. [34:26] And I asked ChatGPT to read it to me. [34:28] And it did it. [34:29] And I was like, oh, my God, this is awesome. [34:33] So I just think there are all these moments where we're just getting started. [34:37] And multimodal, I think, is probably the biggest because phones taught us to talk with our thumbs. [34:44] And I think this new world we're moving into, there's going to be new hardware that just really help us understand that we can talk, we can listen, we can see, we can write. [34:53] We can do all of these things in a very human way that we're just scratching the surface of. [34:58] So let me give you a different frame on that. [35:00] I agree with all of that. [35:03] If you look at what we talked about the internet earlier [35:06] and the bubble associated with it, [35:08] but what the internet did is give you access to a lot more stuff, [35:15] whether it was media, YouTube videos, or TikTok, [35:21] or you name it, information of any sort. [35:26] But it's expanded it to the point where no human can actually use the internet fully. [35:33] I think of AI as given you're limited to 8,000 some hours a day, some of which is meant for sleeping. [35:42] It'll make your time much more efficient. [35:46] So the internet exploded information available to you to the point where you couldn't use it. [35:52] And I think what AI will do is filter it to make your every hour the most effective hour if you know how to use it. [36:00] So intelligence will reduce the world to what is most relevant to you personally. [36:07] And I may have a different set of priorities than Sarah. [36:10] So I think of intelligence as summarizing the world to the most relevant things for me and the most relevant things to her, which are different. [36:20] So I think that's where there's almost unlimited capacity for intelligence to be used to reduce information when the Internet exploded information. [36:32] Yeah, yeah. [36:33] We've talked a lot about the consumer side, and it feels like OpenAI is very much winning the consumer side. [36:39] Question comes up about enterprise, and how is OpenAI going to compete and win in that area? [36:46] So I think we're already winning in this area. [36:50] What I see is, you know, 90% of corporations are saying they either are using OpenAI or intend to use over the next 12 months, right? [37:00] I think the second is Microsoft and Microsoft's using our technology. [37:04] So I actually think we have, this is where the consumer is a really potent part of the enterprise flywheel. [37:10] So as I said earlier, when someone, you know, you back in the day when you first started bringing your iPhone to work and corporates didn't want you to do that, you just discovered you can't say no to the tidal wave that is consumer preference. [37:23] So something I'm already using that I've already got in my pocket and I get to work, my expectation is work is at least as good, if not better. [37:32] And so that's what's helped drive our actual enterprise business, the fastest company ever to get to 1 million businesses on a platform. [37:41] And we did that in about a year and a half. [37:44] But where to from here? [37:45] Because clearly we're just scratching the surface. [37:48] So some of it is certainly meeting customers in terms of their vertical so that we talk to them in their language. [37:54] And we learned this art of enterprise selling, which is let me not tell you all about my products, but let me understand your problem. [38:02] Like, what is your board forcing on you, Mr. and Mrs. CEO? [38:06] What is the thing your customers most want that you can't deliver? [38:10] OK, let's start putting intelligence against that. [38:13] We can then drop that down into some light vertical specialization to quite heavy vertical specialization. [38:20] things like RLing models that are very pertinent to a use case. [38:25] Like let's say in an energy company, it might be really understanding that particular oil [38:30] well or all the seismic data they have to say, what's the recovery we're going to get [38:35] out of this gas field? [38:37] Like that is deep specialization. [38:39] And then I think it gets the whole way to some of these big transformational research [38:43] projects that we've begun, where we're actually almost taking over someone's whole business [38:48] and helping them rethink it in a smarter, faster, better way that ultimately drives their key business metrics. [38:55] So it's a journey. [38:57] I think most corporates have started with wall-to-wall ChatGPT. [39:01] That's an easy starting point. [39:03] They've done some coding. [39:06] And in many cases, a lot of coding. [39:07] Like when I talk to corporates, CEOs are starting to say things like 60% of all my production code was built by an agent. [39:16] And I'm like, you didn't even know what production code meant 12 months ago. [39:20] But now you're saying that. [39:21] That's good because it means you're tracking it. [39:24] But on agents, it's just starting. [39:27] We only see about 14% of all customers when you go out and just survey U.S. corporates are using something agentic today, 14%. [39:37] When I just explained what's happening in my finance organization. [39:40] So I think we are just getting going. [39:43] But I couldn't be more excited about the opportunity. [39:45] It's huge. [39:46] Okay. But if I'm a startup and I look at everything OpenAI is doing, I might be asking, is there room for me? What do I get to do? [39:56] Look, models will keep getting better and do more and more. But I do believe there's lots of room to build on top. [40:05] You know, no one company can do everything on the planet. There's billions of people who are working whose job AI can help with. [40:16] I don't think OpenAI will specialize in everyone. [40:20] So I think the careful thing to do is be clear where the models will go, OpenAI or others, and what they will be able to do. [40:32] And how do you use that best to then specialize into a more interesting world? [40:39] Some sort of specialization where you add something that's additional to the base models. [40:46] And frankly, just intelligence isn't the only thing to provide a solution. [40:51] There's lots of other stuff that goes around solution beyond intelligence. [40:57] So I think there's lots of opportunity to build on top of these models. [41:01] And the more powerful they get, the number of opportunities to add to it dramatically increases. [41:07] How do you think about, so I think a lot about use cases where there's already a lot of data that's being aggregated, perhaps by that startup, by that company, that, you know, today, I think 95% of the world's information actually sits behind corporate firewalls, university firewalls, and so on. [41:25] So there's, even though we talk about the vast training that's occurred, again, we're just getting going. [41:31] But I think companies that have already built businesses that have aggregated that data have access to it. [41:36] And then on top of that, have managed complex workflows. [41:40] So I often give the example of our procurement system. [41:43] Procurement system per se, not that complicated. [41:46] But what it does very well is it understands things like delegation of authority. [41:50] So it knows what the board has approved in terms of approval limits. [41:54] So it knows that when this software contract comes in, it's over X amount, so only I can approve it. [42:01] Or if it's beneath that, but it knows a VP can approve it. [42:05] It doesn't know that Andrew's a VP, but it knows to touch the HRS system and check what's his level. [42:11] And so the whole procurement flow can happen in a way where I have compliance and governance and hopefully makes just the whole company run faster. [42:20] Those are places I get interested for startups. [42:22] So where have you got access to unique data with a complex workflow? [42:26] It feels like there's more of a moat around that, that we want to work alongside you. [42:30] But, you know, the general purpose model is not going to do all of that itself. [42:35] Yeah, no, I completely buy that. [42:37] I think there's lots of opportunity. [42:39] I've seen quite a few startups around just permissioning around data. [42:44] Yeah. [42:44] Like who can do access to what information. [42:47] For example, I've seen a whole bunch of startups around customizing to each company the models for their history and their priorities. [43:00] And the agent, the whole identity side of agents, I think we're just starting to understand both the risk that can happen when you have agents talking to agents talking to agents, but then also how are you going to permission that and then start to think about like agentic commerce, like the complexity that's coming is also quite big. [43:20] So to suggest there's no more opportunity as a startup, I think it's never been probably more interesting or fun to be a startup. [43:26] Yeah. I think there's more opportunities than there have ever been. [43:30] What are you looking for now? What gets you excited when you talk to a company? [43:33] Well, the hardest thing is great people, always. [43:38] But I think the other thing that has been in short supply is agency, where people sort of have the agency to make things happen. [43:50] That, again, comes down to people, but there's so much opportunity. [43:57] I think traditional things like knowing a space or experiencing space is much less relevant now. [44:03] It's more agency. [44:04] We've not talked about the whole new world of robotics and real world models and all that. [44:11] That's a whole space by itself that we probably don't have time for. [44:15] Well, do we? [44:16] We've got time for that. [44:17] I've got plenty of time. [44:18] I'd love it. [44:19] I want to go there. [44:20] Yeah, because we talked about where we're headed here. [44:22] And you famously talked about kind of the role of 2050 and things are moving fast. [44:27] Models are getting faster and more capable. [44:29] And where do you see things like robotics headed? [44:33] Well, I think two years ago when I gave a talk at TED, I said the robotics business, both [44:41] bipedal and other robots will be a larger business in 15 years than the auto industry is today. [44:49] We think of auto industry as one of the larger businesses on the planet. And this other thing [44:55] will be larger. I don't think there's very many automotive companies who are thinking of the world [45:02] that way. They're thinking about how to use a robot in their assembly line. Not that that business [45:08] is larger than their current business, all driven by the intelligence of robots. [45:15] So massive opportunities for startups there. [45:19] And we are seeing a lot of activities. [45:21] Yeah. [45:22] And I think sometimes we underestimate. [45:24] So when you think about robots in the home, right? [45:26] People, very fertile area, no one's really had a breakthrough, though. [45:30] There's so many different issues around the complexity. [45:33] Actually, sometimes the more time I spend in AI, [45:36] They actually, the more respect I have for the human condition in a way, because our ability to move around the world and do, you know, if you watch like the people in robotics getting so excited about a robot folding clothes, you know, perhaps my 18 year old, I'd be just as excited about. [45:52] But for the average human, I assume they can fold clothes. [45:56] But I think the hello world of robotics. [45:59] But you do get a little stuck in your head that they have to somehow be a human. [46:04] But it turns out there may just be these breakthrough moments, like, for example, companionship in the home. [46:12] Right. We have an aging population. [46:14] What's one of the biggest? [46:15] You know, we talk about epidemics in the world. [46:17] Loneliness is probably one of the biggest epidemics. [46:20] What does someone living alone, maybe has just lost a spouse, value most? [46:25] Just someone to converse with in a way that feels intuitive and human. [46:29] We see people using ChatGPT more and more for this conversation. [46:33] But is there a humanoid-esque breakthrough where it turns out you don't need it to make coffee or full clothes or do the dishes, although that would be good too. [46:42] But it might just be something a little bit more simple that still adds a lot of value and is just the first crawl, walk, run of this kind of future that Vinod is talking about where that whole complex is X times more valuable ever than we saw in automotives. [47:00] I think that it's interesting because we can sort of think of kind of like our present and put robots in places and do things like that. It's really hard to think of when you really have extremely low cost labor manufacturing, etc. And then the world you can build from there because, you know, we can look at that's a good solution for now. But when the cost of building a wonderful state of the art assisted living facility where you can put a bunch of people together, the cost drops. [47:26] I think that's the thing I have. [47:27] The hardest problem is for me is to really think, what does it really mean when you lower the cost? [47:32] We've lowered the cost of intelligence. [47:33] What does it mean we really lower the cost of labor? [47:35] Well, my personal view, sometime probably towards the end of the next decade, you'll see a massively deflationary economy. [47:46] Because labor will be near free. [47:48] Expertise will be near free. [47:51] Most functions will be almost zero cost. [47:55] How it exactly plays out, a little hard to tell. [47:59] How purchasing power versus production of goods and services plays out. [48:03] But I expect we'll see a hugely deflationary economy at a level people aren't planning on. [48:09] So there's social aspects of adaption of AI that hasn't been handled yet. [48:16] I think the conversation we need to have is what will people do? [48:20] I get asked that a lot. [48:23] How will people make a living? [48:27] I think the minimum standard of living governments can assure people is going to be much, much higher without needing to earn an income. [48:38] I mean, I can't imagine much better primary care, like 10x more primary care than today, doesn't happen for a dollar a month. [48:48] I have a hard time imagining how that happens. [48:52] It will be true, it costs almost nothing to have free primary care, free education. [49:00] Almost AI tutors for every person, personal tutors for every child. [49:05] That's already happening. [49:08] So there's a set of services that will be free. [49:12] There's some hard nuts to crack. [49:14] Housing is the hard one. [49:16] You know, for people in the bottom half of the U.S. population, they spend 40 some percent of their income on housing and food. [49:26] So there's some hard nuts. [49:27] But I do think both are addressable by robotics and better approaches. [49:32] Well, this has been a very interesting conversation. [49:35] I'm excited to see where things are headed. [49:36] Thank you both for joining us here on the podcast. [49:39] Thank you. [49:40] Thank you.