DJ Patil has spent the past several months on a listening tour. Wherever he travels, he finds a local university, pings faculty and students and anyone else who wants to show up, and runs an AMA. He’s heard from grad students who can’t get callbacks, hospital administrators dealing with federal policy changes that land like a change in the laws of physics, and executives who can’t forecast their AI spending past six months. He’s trying to synthesize all of it and help reframe the wider conversation.过去几个月，DJ Patil一直在进行一场倾听之旅。无论走到哪里，他都会找一所当地大学，联系教职员工、学生以及任何愿意参加的人，举办一场AMA（问我任何事）。他听到了研究生因得不到回音而苦恼，医院管理者因联邦政策变化（如同物理定律改变）而手忙脚乱，以及高管们无法预测未来六个月的AI支出。他正试图整合所有这些信息，并帮助重新构建更广泛的讨论。
DJ co-coined the term “data scientist,” served as America’s first chief data scientist under President Obama, and was chief scientist at LinkedIn. He’s a longtime O’Reilly author, going back to Building Data Science Teams and Ethics and Data Science, and he’s on the founding team at Devoted Health, where he’s spent the past decade building the kind of data infrastructure most organizations are still struggling to put in place. He calls it “the tidy house.” He sat down with me to talk about “the broken promise” in the job market that is driving AI sentiment, and why weak data infrastructure is a big part of the gap between what AI can do and what most institutions can actually absorb.DJ是“数据科学家”一词的联合创造者，曾担任奥巴马总统领导下的美国首位首席数据科学家，以及领英的首席科学家。他是O'Reilly的长期作者，著有《构建数据科学团队》和《伦理与数据科学》，并且是Devoted Health的创始团队成员，过去十年他一直在那里构建大多数组织仍在努力搭建的数据基础设施。他称之为“整洁之家”。他与我坐下来讨论了就业市场中推动AI情绪的“破碎承诺”，以及为何薄弱的数据基础设施是AI能力与大多数机构实际吸收能力之间差距的重要原因。
The broken promise破碎的承诺
What DJ keeps hearing on his tour is anger and angst. One word that keeps coming up is “terrified.” Workers are worried about layoffs. Meanwhile, students, including those from top-tier universities like MIT, Carnegie Mellon, and UC Berkeley, have been applying to 300+ internships and getting fewer than 10 callbacks. Many had zero offers going into the summer. And the industry’s response has been to tell them to learn more AI and burn more tokens. What it comes down to, DJ explained, is “effectively a broken promise”:DJ在巡演中不断听到的是愤怒和焦虑。一个反复出现的词是“恐惧”。工人们担心裁员。与此同时，包括麻省理工、卡内基梅隆和加州大学伯克利分校等顶尖大学的学生，申请了300多个实习岗位，却只收到不到10个回电。许多人到夏天时一个offer都没有。而行业的回应是让他们学习更多AI，消耗更多代币。DJ解释说，归根结底，这“实际上是一个破碎的承诺”：
We said, “Go to college, get these things, you’re going to get an internship, you’re going to get job training, you’re going to pay off your student loans, and then you’re going to have all the other things that are part of that social contract.”我们说过，“去上大学，获得这些，你会得到实习机会，你会得到职业培训，你会还清学生贷款，然后你会拥有那个社会契约中的所有其他东西。”
What the students are feeling for the first time [is]. . .“Wait, if I can’t get this internship, . . .I’m fundamentally off trajectory from getting this job.” And it doesn’t have to be a technical person. It could be someone that is in marketing. It could be someone that’s in the liberal arts. It could be a researcher. . . .There are plenty of students that I have talked to who are supposed to be going to a doctoral PhD program or a medical school or something like that. The slots aren’t there because of the overall budget impacts. And so whether you call it AI impact or economic reframing, the thing is broken.学生们第一次感受到的是……“等等，如果我得不到这份实习，……我就从根本上偏离了获得这份工作的轨道。”这不一定是技术人员。可能是市场营销的人，可能是文科的人，也可能是研究人员。……我交谈过的许多学生本应攻读博士学位或进入医学院等。但由于整体预算影响，这些名额不存在了。所以无论你称之为人工智能影响还是经济重构，这个体系已经出了问题。
This is where both DJ and I have been trying to build a counter narrative. The story coming from the AI labs is destructive: “We’re going to put all of you out of work, and we’ll figure out the rest once the intelligence explosion arrives.” That’s bad PR for AI, but it’s also magical thinking. An economy is a circulatory system. You can’t put your customers out of work and at the same time expect that the economy will hum along as usual. A catastrophic recession could easily interrupt the funding that keeps AI on its growth path and the concentration of value that they assume will fund universal basic income and an expanded safety net.这正是我和DJ试图构建一个反叙事的地方。来自人工智能实验室的故事是破坏性的：“我们将让你们所有人失业，等智能爆炸到来后我们再解决其余问题。”这对人工智能来说是糟糕的公关，也是一种魔法思维。经济是一个循环系统。你不能让客户失业，同时又期望经济照常运转。一场灾难性的衰退很容易中断维持人工智能增长路径的资金，以及他们假设将用于资助全民基本收入和扩大安全网的财富集中。
That’s why I’m a fan of mechanism design: start from the outcome you want, then figure out the rules of the game that produces it. Right now, they’ve designed a game that concentrates all the value in the hands of AI first movers. They could be designing a game that generates value throughout the economy. But they aren’t building affordances for that.这就是为什么我喜欢机制设计：从你想要的结果出发，然后找出产生这个结果的游戏规则。现在，他们设计了一个将所有价值集中在人工智能先行者手中的游戏。他们本可以设计一个在整个经济中产生价值的游戏。但他们没有为此构建支持条件。
YouTube ContentID is a good example of mechanism design leading to economic value creation. When unauthorized music use by online video creators triggered a backlash from rights holders, YouTube replied to the takedown notices with a way for both the people who owned the music and the people who wanted to use it to get paid. A whole creator economy came out of that design choice. The labs have the same opportunity in front of them and mostly aren’t taking it.YouTube的ContentID是机制设计带来经济价值创造的一个好例子。当在线视频创作者未经授权使用音乐引发版权方反弹时，YouTube以允许音乐所有者和使用者都能获得报酬的方式回应了删除通知。整个创作者经济就源于这一设计选择。人工智能实验室面前有同样的机会，但大多没有抓住。
DJ had one concrete mechanism in mind:DJ想到了一个具体的机制：
Imagine OpenAI and Anthropic and Microsoft. . .get together and [say], “If you’re building something for your local community, we’ll fully subsidize the token cost for some period of time.”. . .We’re talking about marginal token usage relatively on the spectrum of things, but the potential innovation and use of AI to help local communities could be astounding. You’re not putting anybody out of a job with that. . . .You’re filling the holes that already exist in the system.想象一下，OpenAI、Anthropic和微软……聚在一起说：“如果你在为你的本地社区建设什么，我们将在一定时期内全额补贴代币成本。”……从整体来看，这只是边际代币使用，但人工智能帮助本地社区的潜在创新和应用可能令人震惊。这不会让任何人失业……你是在填补系统中已经存在的漏洞。
The OpenAI Foundation just announced it will put $1 billion into public-benefit projects this year, including $250 million aimed at building economic futures. It’s a start. But it mostly seems designed to ameliorate the bad effects of AI rather than to forestall them by building a more inclusive AI future. If the labs start investing in the human-plus-AI economy rather than just studying the job losses, the payoff to local communities could be real.OpenAI基金会刚刚宣布今年将投入10亿美元用于公益项目，其中2.5亿美元旨在建设经济未来。这是一个开始。但这似乎主要是为了减轻人工智能的负面影响，而不是通过构建更具包容性的人工智能未来来预防这些影响。如果实验室开始投资于“人+人工智能”经济，而不仅仅是研究失业问题，那么对本地社区的回报可能是实实在在的。
A makerspace to bridge the internship gap一个缩小实习差距的创客空间
DJ’s plan is to build a bridge. He’s launching a program, basically a makerspace, for students who don’t have an internship this summer. Over two four-week sprints, an initial cohort will get mentors, speakers, and the space to explore whatever they’re interested in. It doesn’t have to be AI. Whether they’re doing investigative journalism, screenwriting, or building civic tech, participants will get some experience with current tools and produce a tangible asset they can use to prove what they know. As I told DJ in our conversation, I think he’s really on to something, and I’d love O’Reilly to be part of what he’s building.DJ的计划是建造一座桥梁。他正在启动一个项目，基本上是一个创客空间，面向今年夏天没有实习机会的学生。在为期四周的两个冲刺阶段中，首批学员将获得导师、演讲者以及探索他们感兴趣的任何事物的空间。不一定非得是人工智能。无论是从事调查性新闻、编剧还是构建公民科技，参与者都将获得一些当前工具的使用经验，并产出一份有形的资产，用以证明他们的知识。正如我在对话中告诉DJ的那样，我认为他确实在做一件有意义的事，我很希望O'Reilly能成为他正在构建的一部分。
There’s a kind of person who has always been at the center of the O’Reilly community and never waited for a job description. High school and college dropouts who started companies, built open source software packages, or otherwise took the future into their own hands. People who looked around, found something that needed doing, and did it. DJ is one of them. He’s a community college kid who learned from a good local library, from the books with the “funny animals” on the cover, and from open source. That path is still open. The early O’Reilly business came out of exactly this instinct. We were a tech-writing consulting shop, and when we ran out of paid work, we wrote manuals that didn’t exist yet but that we thought were needed. Later, when there were big conferences for every corporate technology and none for open source, we ran the first one for Perl. Conferences became a whole new business for us. You look for the gap and you fill it.有一种人始终处于O'Reilly社区的中心，他们从不等待职位描述。高中和大学辍学者创办公司、构建开源软件包，或者以其他方式将未来掌握在自己手中。那些环顾四周，发现需要做的事情并付诸行动的人。DJ就是其中之一。他是一名社区大学的学生，从当地优秀的图书馆、封面有“有趣动物”的书籍以及开源中学习。这条路依然敞开。早期的O'Reilly业务正是源于这种本能。我们是一家技术写作咨询公司，当付费工作枯竭时，我们编写了当时尚不存在但我们认为需要的手册。后来，当每个企业技术都有大型会议而开源却没有时，我们举办了第一届Perl会议。会议成了我们的全新业务。你寻找空白并填补它。
DJ pushes the same idea down to the level of the neighborhood:DJ将同样的理念推及到社区层面：
If you want to feel rewarded, go fix something in your neighborhood. Go help out the food pantry. Go help out the local foster child care system. Go help out. . .parks and rec. Use those skills to go do something, and then you’re going to see. . .people respond in a different way. . . .The target-rich area for problems is massive. You just have to look.如果你想获得成就感，就去修复你社区里的问题。去帮助食品银行。去帮助当地的寄养儿童系统。去帮助……公园和娱乐部门。运用这些技能去做些事情，然后你会看到……人们的反应会不同。……问题密集的区域是巨大的。你只需要去看。
I’ve never bought the jobless-future story. Back when I wrote WTF? in 2016, I pointed out that there is so much around us that needs to be made better. The constraint has never been a shortage of problems. AI gives us new tools for solving them. It should be a way to put people to work, not out of work.我从未相信过“无工作未来”的说法。早在2016年写《WTF?》时，我就指出，我们周围有太多需要改进的地方。限制因素从来不是问题的短缺。人工智能为我们提供了解决这些问题的新工具。它应该是一种让人们就业的方式，而不是失业。
The organization is the AI bottleneck组织是人工智能的瓶颈
DJ has also been visiting hospitals and clinics and talking to CIOs and CTOs as part of the tour, and what he’s seeing is alarming.DJ在巡访期间还参观了医院和诊所，并与首席信息官和首席技术官进行了交谈，他所看到的情况令人担忧。
The federal changes to Medicaid and the Affordable Care Act are landing on systems that were already near collapse. Hospitals that depended on outpatient procedures like colonoscopies for margin are watching volumes drop 20% to 30% because people can’t afford insurance. Some are running $1 million a day behind, a $300 to $400 million shortfall for the year.联邦对医疗补助和平价医疗法案的改革正落在本已濒临崩溃的系统上。依赖结肠镜检查等门诊手术获取利润的医院，其业务量下降了20%到30%，因为人们负担不起保险。有些医院每天落后100万美元，全年缺口达3到4亿美元。
At the same time, AI companies are telling those same hospitals to move into the new world, and partly because of the “you will soon be replaced” narrative from the AI labs, labor is responding the way the Kaiser nurses did in California, where any use of AI was off the table as a bargaining condition. As DJ pointed out, we can’t afford to disregard AI when it has the potential to automate the most painful parts of healthcare workers’ jobs and let them “do the job they’re trained for” without the administrative burden. Businesses need to change not just their narrative but their strategy. They need to be saying, “We’re going to use AI to help you do more for our customers. We’re going to make your job more human and let the machines deal with the BS.”与此同时，人工智能公司正告诉这些医院要迈向新世界，部分原因是AI实验室宣扬的“你很快就会被取代”的说法，导致劳动力像加州凯撒护士那样回应——任何AI的使用都被列为谈判条件。正如DJ指出的，当AI有潜力自动化医疗工作者最痛苦的工作部分，让他们摆脱行政负担，“做他们受过训练的工作”时，我们不能忽视AI。企业不仅需要改变他们的说法，还需要改变他们的策略。他们需要说：“我们将利用AI帮助你为客户做更多。我们将让你的工作更人性化，让机器处理那些琐事。”
There’s a version of this where the efficiencies AI creates get plowed back into better patient care. There’s also the version that’s actually happening in most places, where private equity captures the savings as profit. The difference is institutional design, and that’s where reform isn’t happening. I saw this directly with a Code for America project called Clear My Record. A California initiative had turned a number of petty crimes into misdemeanors, but very few people were petitioning to have their status changed. We started using software to streamline an absurdly convoluted criminal record expungement process, but then we asked ourselves why we were helping people fill out forms that shouldn’t exist. The law had already changed the record. The process should have been a database update, not something that required a petition to the court. That’s the kind of problem AI was born to solve. It can help us refactor old stuck processes and move to something way better.有一种版本是AI创造的效率被重新投入到更好的患者护理中。还有一种版本是大多数地方实际发生的，即私募股权将节省的资金作为利润收入囊中。区别在于制度设计，而这正是改革没有发生的地方。我直接从一个名为“清除我的记录”的“为美国编程”项目中看到了这一点。加州的一项倡议将一些轻罪转为轻罪，但很少有人申请改变他们的身份。我们开始使用软件来简化一个极其复杂的犯罪记录清除流程，但随后我们问自己，为什么我们要帮助人们填写本不该存在的表格。法律已经改变了记录。这个过程应该是一个数据库更新，而不是需要向法院请愿的事情。这就是AI天生要解决的问题。它可以帮助我们重构旧的僵化流程，转向更好的方式。
Done right, DOGE could have been an opportunity to carry out that kind of real institutional change at scale. Instead it became a wrecking ball, and it’s given the whole idea of institutional reform a bad name.如果做得好，DOGE本可以成为大规模实施这种真正制度变革的机会。但它却变成了一颗破坏球，给整个制度改革的理念带来了坏名声。
The Silicon Valley default assumes that incumbents will just get disrupted by startups, the way media was by Google and Meta and retail was by Amazon. There’s some truth to that. But disruption takes much longer than people think, and in a domain as central as healthcare or government services, the delay means real harm to real people. Healthcare is a third of the economy. You can’t just let it fail and rebuild it fresh while people depend on it for survival.硅谷的默认假设是，现有企业会被初创公司颠覆，就像媒体被谷歌和Meta颠覆，零售被亚马逊颠覆一样。这有一定道理。但颠覆所需的时间比人们想象的要长得多，而在像医疗或政府服务这样核心的领域，延迟意味着对真实的人造成真实的伤害。医疗占经济的三分之一。你不能让它失败并重新建立，而人们却依赖它生存。
Data infrastructure is the competitive advantage数据基础设施是竞争优势
DJ’s term for the alternative he’s living with at Devoted is “the tidy house.” He built the boring infrastructure years before LLMs existed, and that’s why the company could move the moment AI arrived. People don’t think about having well organized, effective data infrastructure as the deep secret behind enterprise AI adoption, but DJ is right. As we work on O’Reilly’s own transformation and talk with our customers about what’s holding them back, it’s a huge part of the problem.DJ 用“整洁的房子”来形容他在 Devoted 所经历的替代方案。他在 LLM 出现多年前就建立了枯燥的基础设施，这就是为什么公司能在 AI 到来时立即行动。人们不认为拥有组织良好、有效的数据基础设施是企业采用 AI 的深层秘密，但 DJ 是对的。当我们致力于 O'Reilly 自身的转型，并与客户讨论阻碍他们的因素时，这确实是问题的一大部分。
One of the ways we’ve tried to make this work is fundamentally still data 101, unified data environments, data flows that are clean, that have a lot of organization. . . .Because we invested so heavily in that infrastructure, the dumb, boring, painful parts of making sure you’ve got a really great data warehouse, great data engineering pipes, all of the metadata that goes with it, when AI shows up, you get to use it right away. Now you get to focus on the orchestration, the harness, all those pieces.我们尝试使其发挥作用的方法之一，本质上仍然是数据 101：统一的数据环境、干净且组织良好的数据流……因为我们在这方面投入了大量资金，那些确保拥有出色数据仓库、优秀数据工程管道以及所有相关元数据的枯燥、乏味、痛苦的部分，当 AI 出现时，你就能立即使用它。现在你可以专注于编排、工具链等所有环节。
While other organizations are reconstructing ETL inside context windows and paying for it in GPU costs, Devoted’s team gets to work on the actual clinical problems. As DJ put it, transforming a healthcare system is “like walking and chewing gum while balancing bowling balls on your head and on a unicycle,” with the laws of physics changing on you the whole time. The organizations that come through it will be the ones that did the unglamorous work of keeping clean, flowing data with its lineage and metadata intact. The ones that didn’t will keep paying to reconstruct context they should have had all along.当其他组织在上下文窗口中重建 ETL 并为此支付 GPU 成本时，Devoted 的团队却能专注于实际的临床问题。正如 DJ 所说，改造医疗系统就像“一边走路一边嚼口香糖，同时头上顶着保龄球并骑独轮车”，而物理定律还在不断变化。能够挺过来的组织将是那些完成了不引人注目的工作——保持数据干净、流动，并保留其血统和元数据的组织。而那些没有这样做的组织将继续为重建本应一直拥有的上下文而付出代价。
The pharmacists who built their own agents自己构建代理的药剂师
The tidy house pays off when you put the tools in the hands of people who already know the domain. At Devoted, clinicians are building things without waiting for a product manager to learn the problem first. These frontline workers have already spent decades understanding it.当你将工具交到已经熟悉领域的人手中时，整洁的房子就会得到回报。在 Devoted，临床医生正在构建东西，而无需等待产品经理先了解问题。这些一线工作者已经花了数十年时间理解它。
A pharmacist. . .says, “Hey, you know what? I’m really worried when I see these kinds of drugs show up together. That’s not a good thing. . . .Why don’t I have an agent that alerts me every time this happens? I should just automate it because maybe one of the patients gets prescribed something by another provider and we don’t see it.” So the pharmacist [says,]. . .”I’m just going to build that agent.” Now I’ve got an agent always looking for bad drug interactions. And another pharmacist says, “I’ve got my own version of that.” . . .So I say, “Hey, agent, I want you to go ask all the pharmacists that we have a quick survey of what might be happening. . . .What are the universe of things that we should be watching out for?” Now I’ve got a robust medical layer. . .looking out and protecting all of our members from bad drug interactions. Having the right infrastructure makes it possible to act on decades of accumulated judgment distributed throughout the organization.一位药剂师……说：“嘿，你知道吗？当我看到这些药物同时出现时，我非常担心。这不是好事……为什么我没有一个代理每次发生这种情况时提醒我？我应该把它自动化，因为也许某个患者被另一位医生开了药，而我们没看到。”于是药剂师说……“我就要构建那个代理。”现在，我有一个代理一直在寻找不良药物相互作用。另一位药剂师说：“我有自己的版本。”……所以我说：“嘿，代理，我想让你去快速调查所有药剂师，看看可能发生了什么……我们应该警惕哪些情况？”现在，我有一个强大的医疗层……在保护我们所有成员免受不良药物相互作用的影响。拥有正确的基础设施使得能够利用组织中分布的数十年积累的判断力。
The histogram is still the most powerful product直方图仍然是最强大的产品
You don’t need exotic tooling to get value out of data, and DJ punctured the assumption that you do.你不需要奇特的工具就能从数据中获取价值，而DJ戳破了这种假设。
Oftentimes, I tell people, the most powerful data product you can build is still a histogram. Just give me a distribution of what’s going on. . . .AI gives us a tremendous opportunity to let people [access this data quickly], but we’ve got to figure out the guardrails, so people don’t ask [questions] or get answers. . .[without realizing] that there’s a flaw in how they’re asking it.我经常告诉人们，你能构建的最强大的数据产品仍然是一个直方图。只要给我一个正在发生的事情的分布。……人工智能给了我们一个巨大的机会让人们快速访问这些数据，但我们必须设置好护栏，这样人们就不会在提问或获取答案时……没有意识到他们提问的方式存在缺陷。
Every time a new technology empowers employees to make innovative use of corporate data, there is resistance. We’ve been in this loop since the beginning of the data movement, DJ explained. The stewards of the data warehouse stand at the gate and say, “You shall not pass!” Then democratization breaks it open, and the gatekeepers reconstitute themselves in the next era. Hadoop did it last time. LLMs are doing it now, and the temptation to insist that only experts can use the tools correctly is as strong as it’s ever been. You do need ways to catch errors. But the goal should always be access.每当一项新技术让员工能够创新地使用公司数据时，总会遇到阻力。DJ解释说，自数据运动开始以来，我们就一直处于这个循环中。数据仓库的管理员站在门口说：“你不许过去！”然后民主化打破了它，守门人在下一个时代重新集结。上次Hadoop做到了这一点。现在LLM正在做同样的事，而坚持只有专家才能正确使用工具的诱惑比以往任何时候都强烈。你确实需要方法来捕捉错误。但目标始终应该是访问。
The real opportunity is in the layers above AI models真正的机会在于AI模型之上的层次
DJ and I also talked about the new discipline forming inside computer science, engineering the trade-offs between conventional software and LLMs, when to reach for a local or open weight model, and understanding what inference actually costs against the value it returns.DJ和我还谈到了计算机科学中正在形成的新学科，即在传统软件和LLM之间进行权衡设计，何时使用本地或开放权重模型，以及理解推理相对于其返回价值的实际成本。
Getting that right requires an expanded view of mechanism design. While this isn’t how economists talk about it, many advances in technology are really just that: redesigning the rules of a game to get better outcomes. Pay-per-click advertising started as a crude auction that sold to the highest bidder, and then Google refined it into something that worked. Rob McCool wired a web server to a database with CGI and ushered in a decade of invention of new mechanisms for data-driven websites. Or take Apache Kafka, which DJ reminded us began as a project to help LinkedIn rein in its Splunk bill and only later became the foundation for a company and an ecosystem.要正确做到这一点，需要扩展对机制设计的理解。虽然经济学家不是这样说的，但许多技术进步实际上就是如此：重新设计游戏规则以获得更好的结果。按点击付费广告最初是一种粗放的拍卖，出价最高者得，然后谷歌将其改进为有效的机制。Rob McCool用CGI将Web服务器连接到数据库，开启了数据驱动网站新机制的十年发明。或者以Apache Kafka为例，DJ提醒我们，它最初是为了帮助LinkedIn控制其Splunk账单而启动的项目，后来才成为一家公司和生态系统的基础。
We’re at the front of an architectural innovation cycle now, and the biggest opportunities are not in the models themselves but in the layers above them. That’s also where a renaissance of open source for the AI era could happen.我们现在正处于架构创新周期的前沿，最大的机会不在于模型本身，而在于它们之上的层次。这也是AI时代开源复兴可能发生的地方。
DJ and I are both, as he says, “this giant human LLM, summarizing and distilling all the things we’re hearing” from a lot of people. What we’re hearing is that the technology is mostly ready, but our institutions are not. What’s lagging is the organizational and economic infrastructure that lets universities, hospitals, data teams, and the labs themselves actually deploy what’s been built.DJ和我都像他所说的那样，是“这个巨大的人类LLM，总结和提炼我们从很多人那里听到的一切”。我们听到的是，技术基本已经准备好了，但我们的机构还没有。落后的是组织和经济基础设施，这些基础设施让大学、医院、数据团队和实验室本身能够实际部署已经构建好的东西。
It’s time to get busy!是时候忙碌起来了！
On June 10, Harper Reed, cofounder of 2389 Research, will join me to talk about why the future of software depends on creativity, serendipity, and building weird stuff. And on July 9, Trail of Bits cofounder and CEO Dan Guido will stop by to share his playbook for going AI native. You can register to attend them live here. You can also follow Live with Tim O’Reilly on YouTube, Spotify, Apple, or wherever you get your podcasts.6月10日，2389 Research的联合创始人Harper Reed将和我一起讨论为什么软件的未来取决于创造力、偶然性和构建奇怪的东西。7月9日，Trail of Bits的联合创始人兼CEO Dan Guido将分享他关于如何实现AI原生的策略。你可以在这里注册参加直播。你也可以在YouTube、Spotify、Apple或其他播客平台上关注《与Tim O'Reilly一起直播》。