WEBVTT 00:00:00.000 --> 00:00:03.277 Do you think you have AGI? 00:00:03.360 --> 00:00:06.997 I think about this a lot. I talked to I talked to different people about where 00:00:07.080 --> 00:00:09.357 they believe we're at. And the best 00:00:09.440 --> 00:00:14.917 answer I've gotten from our team and I it really resonates for me is that AGI 00:00:15.000 --> 00:00:18.917 is a gradual thing, right? It's not really like a moment in time, like a you know 00:00:19.000 --> 00:00:21.717 >> So you talked about it like a moment in time at one point. 00:00:21.800 --> 00:00:22.797 >> That's right. >> definitely did. 00:00:22.880 --> 00:00:25.317 >> That's right. That's what we used to think, right? We we really when we set 00:00:25.400 --> 00:00:28.237 out, we almost thought of it as nothing and then we'll put together the pieces 00:00:28.320 --> 00:00:29.797 and that's the day of AGI. 00:00:29.880 --> 00:00:33.077 >> Greg Brockman's point is important. AGI 00:00:33.160 --> 00:00:35.277 may not arrive as one dramatic 00:00:35.360 --> 00:00:38.237 breakthrough. Instead, we may already be 00:00:38.320 --> 00:00:40.877 watching it emerge gradually. As AI 00:00:40.960 --> 00:00:42.757 moves from answering questions to 00:00:42.840 --> 00:00:48.080 actually completing complex work on our behalf. 00:00:50.120 --> 00:00:53.237 >> Well, I think we started late. And 00:00:53.320 --> 00:00:57.677 specifically the way to think about the overall field is that for the past 00:00:57.760 --> 00:01:01.277 couple years the chat market was the main growing one. And the reason for 00:01:01.360 --> 00:01:04.877 this was simple. GPT-4 was the first model where the results were 00:01:04.960 --> 00:01:08.757 personalized enough that it made sense for you to ask a question, read the 00:01:08.840 --> 00:01:10.677 answer. It's interesting to you. And so 00:01:10.760 --> 00:01:16.077 it very naturally lends itself to a chat. Right? To a chat bot. And that is 00:01:16.160 --> 00:01:19.917 something we're able to scale, deliver value. Now a billion people every single 00:01:20.000 --> 00:01:22.037 week use ChatGPT. Like a billion people. 00:01:22.120 --> 00:01:23.757 Like that is scale. That is real scale. 00:01:23.840 --> 00:01:27.757 No one else is doing that. But this year something else happened. Is the agentic 00:01:27.840 --> 00:01:30.237 moment. This really was December of last 00:01:30.320 --> 00:01:33.637 year is when both we and competition had 00:01:33.720 --> 00:01:37.757 a moment where agents were really able to first start doing software 00:01:37.840 --> 00:01:40.917 engineering in a significant way. It went from saying that, you know, you 00:01:41.000 --> 00:01:44.117 could speed up your work with maybe 20% 00:01:44.200 --> 00:01:48.357 productive productivity increase to 80% of the work could now be done with an 00:01:48.440 --> 00:01:53.557 AI. And so suddenly the way that software engineers operated it inverted 00:01:53.640 --> 00:01:57.997 that you became much more of a manager of agents. And since then the growth has 00:01:58.080 --> 00:02:00.637 been unprecedented, absolutely unprecedented how these agents have been 00:02:00.720 --> 00:02:04.277 adopted. >> And this shift is already visible in 00:02:04.360 --> 00:02:06.317 software development. The important 00:02:06.400 --> 00:02:09.477 change isn't simply that AI can write 00:02:09.560 --> 00:02:12.077 better code. It's that developers are 00:02:12.160 --> 00:02:16.277 beginning to hand entire tasks to agents 00:02:16.360 --> 00:02:19.157 and increasingly act as managers rather 00:02:19.240 --> 00:02:21.597 than doing every step themselves. 00:02:21.680 --> 00:02:26.437 >> I asked Codex, and this is just off-the-shelf Codex, no special access, 00:02:26.520 --> 00:02:29.797 no special models. I just use my personal Codex to say, 00:02:29.880 --> 00:02:33.317 "Scan gregbrockman.com. Tell me what vulnerabilities are there." 00:02:33.400 --> 00:02:35.237 And gregbrockman.com is very simple. 00:02:35.320 --> 00:02:38.437 It's just a static site. I was like, "Come on, what what's it going to find?" 00:02:38.520 --> 00:02:40.997 And it came back with like 13 findings. 00:02:41.080 --> 00:02:44.077 And each one of them, I was like, "You know, I kind of knew about this, but 00:02:44.160 --> 00:02:47.717 it's so much work to fix it. And like, oh yeah, I kind of forgot that I got 00:02:47.800 --> 00:02:51.157 that open. Or you know, here's some domain that was still, you know, it was 00:02:51.240 --> 00:02:54.797 was legacy. I'm not using it anymore. I hadn't set the records to prevent people 00:02:54.880 --> 00:02:57.917 from spoofing your email address." A bunch of little things like that. That 00:02:58.000 --> 00:03:01.557 one seems like a big deal. Well, yeah, you're right now that you say it. Now, 00:03:01.640 --> 00:03:05.997 here's the thing. Honestly, to fix those kinds of things, you have 00:03:06.080 --> 00:03:08.797 to set up like DMARC to do it right. And there's this like whole like process you 00:03:08.880 --> 00:03:11.277 have to do. Well, I'm just like, I don't know, it just takes time. It takes a lot 00:03:11.360 --> 00:03:13.957 of expertise. So, instead I just asked Codex, "Can you fix it?" 00:03:14.040 --> 00:03:17.077 >> Right. >> And so, it opened up my Cloudflare control panel 00:03:17.160 --> 00:03:19.837 and just used computer use. It clicked around. It like set a bunch of headers, 00:03:19.920 --> 00:03:22.477 changed a bunch of settings. I used to have it so that I had Cloudflare in 00:03:22.560 --> 00:03:26.957 front and then AWS on the back end. I was going over insecure HTTP here. Um 00:03:27.040 --> 00:03:30.477 Codex had figured that out. Actually, I'm not even sure how it how it saw that 00:03:30.560 --> 00:03:33.477 cuz I thought everything was hidden behind Cloudflare. It actually was just 00:03:33.560 --> 00:03:36.437 like, "You know what? I'm just going to migrate you off of AWS." So, it set up 00:03:36.520 --> 00:03:40.917 Cloudflare pages, moved over the content. I had an old jQuery version 00:03:41.000 --> 00:03:45.037 that was insecure. It just got rid of jQuery entirely. And it was like 15 00:03:45.120 --> 00:03:48.757 minutes to find all these findings. It was another hour to fix all of them. And 00:03:48.840 --> 00:03:52.277 for me, I just was the manager. I just gave it the intent. I said, "What I want 00:03:52.360 --> 00:03:55.477 is I want to know where the problems are and I want you to fix them." And it did 00:03:55.560 --> 00:03:58.597 it. And that to me is more the future that we're promised. 00:03:58.680 --> 00:04:01.437 >> But perhaps the biggest acceleration 00:04:01.520 --> 00:04:04.517 happens when these systems are used to 00:04:04.600 --> 00:04:07.597 improve AI itself. Brockman says 00:04:07.680 --> 00:04:10.157 engineering problems that once took 00:04:10.240 --> 00:04:12.717 weeks can sometimes be investigated in 00:04:12.800 --> 00:04:18.477 hours with Codex. And that creates potentially powerful feedback loop. 00:04:18.560 --> 00:04:21.197 >> Well, I would think of this as an overall pacing, right? An overall pacing 00:04:21.280 --> 00:04:24.317 of of that frontier. Well, back and another thing to think 00:04:24.400 --> 00:04:27.637 about is the fact that the 00:04:27.720 --> 00:04:32.357 our ability to produce these models is also and to get those capability gains 00:04:32.440 --> 00:04:36.597 is also increasing, right? Our raw capacity is because partly we understand 00:04:36.680 --> 00:04:39.437 this technology a lot better than we did in the past. And that's actually very 00:04:39.520 --> 00:04:43.717 beneficial from a safety and alignment perspective, too, right? That we really 00:04:43.800 --> 00:04:46.997 understand a lot of how the technology works. There's still many many pieces 00:04:47.080 --> 00:04:49.957 that we don't understand, but our ability to engineer it and to really do 00:04:50.040 --> 00:04:54.637 good job of of it technically, um I think has has increased a lot. Um and 00:04:54.720 --> 00:04:57.957 that there are certain problems that really used to hold us back that are 00:04:58.040 --> 00:05:01.437 much diminished. For example, these big training runs, you know, they 00:05:01.520 --> 00:05:05.357 run for many months. And so, typically we'd launch them from a branch of code 00:05:05.440 --> 00:05:08.837 and so we'd be adding little fixes to that that are specific to that run. At 00:05:08.920 --> 00:05:12.637 the same time, everyone's continuing their main line development. And every 00:05:12.720 --> 00:05:14.877 so often there's something on the main line that we're like, "Oh, we really 00:05:14.960 --> 00:05:17.877 want that change. We really want that fix." And then we're like, "All right, 00:05:17.960 --> 00:05:20.397 we got to bring these two branches of development together." And then we merge 00:05:20.480 --> 00:05:23.557 them and then we launch the run and we find that there's a bug because no one 00:05:23.640 --> 00:05:27.117 has actually tested all these main line fixes at the massive scale cuz we only 00:05:27.200 --> 00:05:29.037 have one instance where we can do it. 00:05:29.120 --> 00:05:32.157 And then we spend the next three to four weeks with like all of our best people 00:05:32.240 --> 00:05:35.637 scouring trying to figure out where did the bug come from cuz you can't possibly 00:05:35.720 --> 00:05:40.477 read all of the code that's been written. Like no human can possibly do that. Today, 00:05:40.560 --> 00:05:44.077 you're running at that same type of issue, Codex just reads all the code, right? 00:05:44.160 --> 00:05:46.357 Finds the bug. It says, "Here it is. No problem." 00:05:46.440 --> 00:05:50.357 And so there's something about the partnership of 00:05:50.440 --> 00:05:53.437 this amplification of what the models can do that knock out whole classes of 00:05:53.520 --> 00:05:56.877 problems where we know it would take 3 to 4 weeks and now takes a few hours. 00:05:56.960 --> 00:06:00.517 We've seen this concretely multiple times. And so what I'm getting at is 00:06:00.600 --> 00:06:05.597 that there are a lot of limitations for how we've had to develop in the past 00:06:05.680 --> 00:06:08.597 that have made it a lot harder for us to move forward on problems that are 00:06:08.680 --> 00:06:12.517 important. And now we have the opportunity to move forward on them 00:06:12.600 --> 00:06:15.517 in a much more efficient fashion. And 00:06:15.600 --> 00:06:20.477 that that to me is like let's use that extra juice to 00:06:20.560 --> 00:06:23.957 increase the alignment properties, to increase the safety properties. Let's 00:06:24.040 --> 00:06:26.597 make sure that we have models that monitor other models in a trustworthy 00:06:26.680 --> 00:06:29.757 fashion. Let's really implement some of these ideas that we were talking about 00:06:29.840 --> 00:06:33.437 back in 2017, right? That we have things like 00:06:33.520 --> 00:06:37.997 iterated amplification, things like debate where the models can really spend 00:06:38.080 --> 00:06:41.957 a bunch of compute in order to make sure that what they're doing is actually 00:06:42.040 --> 00:06:44.757 reasonable. To me that's the opportunity in front of us. 00:06:44.840 --> 00:06:48.037 >> And this brings us back to the real question. 00:06:48.120 --> 00:06:51.317 If AI can write software, use tools, 00:06:51.400 --> 00:06:56.197 perform increasingly long tasks, and even help researchers build the next 00:06:56.280 --> 00:07:00.557 generation of AI, at what point do we actually start 00:07:00.640 --> 00:07:03.397 calling it AGI? >> And I think what we've learned over the 00:07:03.480 --> 00:07:05.797 years is that was a naive perspective. 00:07:05.880 --> 00:07:08.677 And you can see this in the evidence of like how 00:07:08.760 --> 00:07:14.317 smooth in many ways the creation of these agents has been, how the 00:07:14.400 --> 00:07:16.877 transformation of software engineering has been, now the transformation of 00:07:16.960 --> 00:07:20.757 knowledge work generally, how cyber capabilities, all these things, they're 00:07:20.840 --> 00:07:23.757 gradually increasing. It's like one of these things where there are moments 00:07:23.840 --> 00:07:25.397 where you realize what has happened. 00:07:25.480 --> 00:07:28.917 Hugging Face is a moment where you realize where you are, but it's all 00:07:29.000 --> 00:07:31.837 gradual. Like if you look back at kind of these 00:07:31.920 --> 00:07:34.157 different models that have been released over time and their performance on 00:07:34.240 --> 00:07:38.997 different benchmarks, just you know sort of that that exponential. 00:07:39.080 --> 00:07:43.677 And so I think that being said, the rest of the answer is that if you go 00:07:43.760 --> 00:07:48.037 if we go forward two years and we look back, I think that that we will say this 00:07:48.120 --> 00:07:51.197 is when AGI was created. So, I think we are going through that moment. 00:07:51.280 --> 00:07:54.797 >> But, it's not it's not something where you can just say it now. 00:07:54.880 --> 00:07:57.477 >> I don't think that it's ever going to be as simple as you can say this was the 00:07:57.560 --> 00:08:01.397 date. There's so many axes, and I think that maybe one lesson in general to the 00:08:01.480 --> 00:08:03.317 whole AGI conversation and why it's so 00:08:03.400 --> 00:08:05.637 complicated is because humans, I think, 00:08:05.720 --> 00:08:08.917 are capable of much more than 00:08:09.000 --> 00:08:13.797 a naive view would give credit to. I think that the diversity of what we do, 00:08:13.880 --> 00:08:17.077 how we set goals, how we accomplish goals, how tasks work, how jobs work, 00:08:17.160 --> 00:08:19.637 all these things extremely deep. 00:08:19.720 --> 00:08:21.237 And so, I think that what we're seeing 00:08:21.320 --> 00:08:23.997 is that there's different aspects of 00:08:24.080 --> 00:08:29.717 either how people accomplish tasks or limitations that are hard for humans to 00:08:29.800 --> 00:08:33.077 overcome, like the thing I mentioned with read all the code, where these AIs 00:08:33.160 --> 00:08:38.157 are becoming able to fill in those gaps, are able to do more and more, and that 00:08:38.240 --> 00:08:42.277 humans it turns out that there are some things that we also really find are 00:08:42.360 --> 00:08:45.957 extremely important for it to be a human there. Like, where does the judgment 00:08:46.040 --> 00:08:49.197 come from ultimately? Where does the taste come from? Who decides what's good 00:08:49.280 --> 00:08:52.637 and what's not good? Like, that is something that should be uniquely human. 00:08:52.720 --> 00:08:56.677 And I'll tell you, just to give you an example, one thing we've seen with image 00:08:56.760 --> 00:09:00.437 generation, when you just generate an image with ChatGPT and you show it to 00:09:00.520 --> 00:09:04.197 someone, no one cares. It is uninteresting to anyone besides you. 00:09:04.280 --> 00:09:09.157 But, if you take a photo of your family and you say turn this into the 00:09:09.240 --> 00:09:12.557 into a caricature, turn this into, you know, a cartoon style, 00:09:12.640 --> 00:09:15.597 people love it. Goes viral. And so, there's something about that 00:09:15.680 --> 00:09:19.717 element of humanity that matters so deeply. People want to connect with 00:09:19.800 --> 00:09:24.157 other people. And that is actually, in in my mind, the purpose of this 00:09:24.240 --> 00:09:28.317 technology, the thing that I think is is really emerging is for deepening human 00:09:28.400 --> 00:09:31.517 connection so you can spend more time with your family, can spend more time 00:09:31.600 --> 00:09:34.437 with people you want to spend time with, but less time, you know, typing away at 00:09:34.520 --> 00:09:37.037 a machine, less time, you know, getting carpal 00:09:37.120 --> 00:09:39.997 tunnel, the whole thing. And so, I think 00:09:40.080 --> 00:09:45.237 that there is something about what AGI is as it's starting to unfold, it's 00:09:45.320 --> 00:09:48.317 almost this like fog that's lifting, it's almost teaching us more about 00:09:48.400 --> 00:09:50.397 ourselves and what it means to be human. 00:09:50.480 --> 00:09:55.717 >> OpenAI's vision also appears to be moving beyond one universal definition 00:09:55.800 --> 00:10:01.157 of AI. For an individual user, the more practical milestone may be something 00:10:01.240 --> 00:10:05.237 much simpler. An AI that knows your context, has 00:10:05.320 --> 00:10:09.717 access to your tools, and can actually accomplish things for you. 00:10:09.800 --> 00:10:14.597 >> What people do see is how chat is evolving, how Codex is evolving, and one 00:10:14.680 --> 00:10:17.797 of your big priorities in this role, as I understand it, is what you guys call 00:10:17.880 --> 00:10:22.237 the merge internally, which is creating this super app, this combined chat GPT 00:10:22.320 --> 00:10:24.757 and Codex. Um and you've got chat GPT 00:10:24.840 --> 00:10:27.157 for work now and the and the tabs. 00:10:27.240 --> 00:10:31.157 Um but at the same time, like it doesn't feel like that's the end state, you 00:10:31.240 --> 00:10:35.077 know? And I'm a user of it and I see realness in how it's uh currently 00:10:35.160 --> 00:10:38.477 executed. Some things don't sync. You can tell you're still combining tech 00:10:38.560 --> 00:10:42.837 stacks. And I'm curious once you're done with that work, what is the end product 00:10:42.920 --> 00:10:46.277 for people that you imagine? >> A personal AGI. 00:10:46.360 --> 00:10:49.477 >> Personal AGI. And what How does that manifest as software? 00:10:49.560 --> 00:10:53.637 >> Yeah, it's it's it's a really really exciting 00:10:53.720 --> 00:10:57.797 vision in my mind, right? Because where we started with chat GPT, it was a text 00:10:57.880 --> 00:11:00.757 box, you could ask anything. But if you think about it, it was 00:11:00.840 --> 00:11:05.037 limited in a very fundamental way. The only thing it could do is output some 00:11:05.120 --> 00:11:08.557 text for you. The only impact it could have in the world was, you know, kind of 00:11:08.640 --> 00:11:13.517 in your brain, right? That it wasn't able to look at your calendar, it wasn't able 00:11:13.600 --> 00:11:15.197 to look at your email. 00:11:15.280 --> 00:11:18.077 And all of that is now changed. 00:11:18.160 --> 00:11:22.237 Chat GPT work is really, by the way, a Codex harnessed in the cloud, right? So, 00:11:22.320 --> 00:11:26.837 we had chat that was this, you know, question-answer system. Um iterate 00:11:26.920 --> 00:11:29.957 turns, you know, people actually then go beyond just, you know, their questions 00:11:30.040 --> 00:11:34.037 to, you know, health, like all these kinds of queries they're able to help them. 00:11:34.120 --> 00:11:37.517 We had Codex, which was an agentic system that can go do work. 00:11:37.600 --> 00:11:41.677 And so obvious it's like chat has to become agentic. And so the place we 00:11:41.760 --> 00:11:44.277 started at the beginning of the year was to say, well, let's let's upgrade chat. 00:11:44.360 --> 00:11:48.797 Let's, you know, make it also do agent stuff. And it just turned out to be so 00:11:48.880 --> 00:11:52.517 hard to execute because now we have two agentic stacks and then we compare 00:11:52.600 --> 00:11:54.917 between one and the other and there'll be something that will work in this one. 00:11:55.000 --> 00:11:58.197 We're like, why isn't it working in that one? And it was just so slow. And so the 00:11:58.280 --> 00:12:01.557 decision we made instead is say, let's take the agentic stack we have, the 00:12:01.640 --> 00:12:04.437 Codex stack, and bring it to ChatGPT. 00:12:04.520 --> 00:12:07.997 And so what you see with chat work is exactly right. This is an intermediate 00:12:08.080 --> 00:12:11.877 state. In some ways it's kind of funny if you think about 00:12:11.960 --> 00:12:16.637 it. ChatGPT was a text box. ChatGPT work as a text box. But this text box is way 00:12:16.720 --> 00:12:18.677 more awesome. It way more things. 00:12:18.760 --> 00:12:21.997 Exactly. And so where we want to be is you should 00:12:22.080 --> 00:12:24.357 it should be simple. User should not have to think about this. You shouldn't 00:12:24.440 --> 00:12:27.517 have to think about tabs or switching and shouldn't have model pickers. You 00:12:27.600 --> 00:12:30.597 shouldn't have thinking levels. You should have an AGI. You should be able 00:12:30.680 --> 00:12:33.797 to ask it. And in fact, you shouldn't even have to ask. It should proactively 00:12:33.880 --> 00:12:38.677 say, hey, I noticed that your favorite band, cuz it knows you well, is in town. 00:12:38.760 --> 00:12:42.277 Tickets went on sale. I got you the tickets exactly where you want for you 00:12:42.360 --> 00:12:46.077 and your family. And you know, I only had 10 minutes to do it cuz everything 00:12:46.160 --> 00:12:48.357 is going to go so fast. So I just purchased it proactively. Hope that's 00:12:48.440 --> 00:12:51.957 okay. And if you've built enough trust with the agent so you've given it 00:12:52.040 --> 00:12:54.317 permission to do that and it kind of knows that well, these tickets are 00:12:54.400 --> 00:12:56.797 within your your price range, within your budget. These other ones, oh no, 00:12:56.880 --> 00:12:58.797 no, I I got to get permission for that. 00:12:58.880 --> 00:13:03.797 Right? That's the kind of system that we want. So trust is the keyword. EQ also 00:13:03.880 --> 00:13:06.797 very important that if you've shared some information, like you've talked 00:13:06.880 --> 00:13:10.117 about a health condition, maybe that's okay information for the AI to talk 00:13:10.200 --> 00:13:11.677 about with a parent, but maybe it's not 00:13:11.760 --> 00:13:13.757 okay to talk about with a stranger. 00:13:13.840 --> 00:13:19.117 Right? All of that judgment and and also observability and really helping you 00:13:19.200 --> 00:13:23.277 build up that trust relationship with the AI so that you know what 00:13:23.360 --> 00:13:26.437 you know, when it's operating on your behalf, it'll pursue your goals. That's 00:13:26.520 --> 00:13:29.597 very, very critical. But the same thing, same kind of 00:13:29.680 --> 00:13:33.757 technology, the underlying AGI stack, one stack, one way of doing connectors, 00:13:33.840 --> 00:13:37.477 so it's able to hook up to this context, also applies to the enterprise. And 00:13:37.560 --> 00:13:40.477 we're really seeing it not just for software engineering, which by the way 00:13:40.560 --> 00:13:42.237 has been totally transformed this year. 00:13:42.320 --> 00:13:45.037 And to the point of AGI being a gradual thing, or a thing where we just 00:13:45.120 --> 00:13:49.117 constantly update our expectations, like we all kind of forget what software was 00:13:49.200 --> 00:13:53.077 1 year ago. Totally different. A very interesting thing within OpenAI is that 00:13:53.160 --> 00:13:56.637 we're at basically 100% adoption of ChatGPT work across every function, 00:13:56.720 --> 00:14:00.597 right? Sales, finance, everyone. And whenever, you know, we roll out a new 00:14:00.680 --> 00:14:03.557 model and it goes away, or, you know, the system breaks for whatever internal 00:14:03.640 --> 00:14:07.077 reason, um everyone's so upset. I cannot do my 00:14:07.160 --> 00:14:10.077 work. And you're like, I don't get it. 6 months ago you didn't have this and you 00:14:10.160 --> 00:14:12.997 were you were working just fine. Like, why are you, you know, why are you 00:14:13.080 --> 00:14:16.677 complaining? And you realize people adapt so quickly and learn how to get 00:14:16.760 --> 00:14:20.757 the most out of these tools. And once you're there, you also just sort of 00:14:20.840 --> 00:14:22.237 forget that there was any other way. 00:14:22.320 --> 00:14:26.397 >> And the implications of that are that what the coding AI tools have done to 00:14:26.480 --> 00:14:31.397 engineering, this could do to all knowledge work, all kinds of fields. And 00:14:31.480 --> 00:14:34.117 that's how you guys used to talk about AGI is like, 00:14:34.200 --> 00:14:37.877 you know, it was basically AI that could do most economically viable work better 00:14:37.960 --> 00:14:40.917 than humans. And what you're describing now in the product kind of sounds like 00:14:41.000 --> 00:14:43.357 that. >> I think that the amplification we've 00:14:43.440 --> 00:14:46.597 seen in software engineering we expect across every field for sure. And there 00:14:46.680 --> 00:14:51.317 is something that I think that we is also worth really thinking about, 00:14:51.400 --> 00:14:55.717 which is the software engineers, rather than having more vacation time 00:14:55.800 --> 00:14:59.397 and having more chill job, they're all working harder than ever before. 00:14:59.480 --> 00:15:00.917 >> Yeah, you guys work a lot of hours here. 00:15:01.000 --> 00:15:04.717 >> Yes. I think across the whole industry people are talking about that. Because 00:15:04.800 --> 00:15:07.757 now you feel like if your agents aren't running, 00:15:07.840 --> 00:15:09.877 it's just wasted opportunity. Right? 00:15:09.960 --> 00:15:13.437 It's like, and I know that feeling because we have spent years of we have 00:15:13.520 --> 00:15:16.317 these GPU clusters, you're either running something on them or you've lost 00:15:16.400 --> 00:15:19.637 the opportunity. You can't get that time back. It's not like the GPU hours that 00:15:19.720 --> 00:15:22.917 you're not using while you're asleep overnight, you can bank them up and use 00:15:23.000 --> 00:15:27.637 them later. It's use it or lose it. And that is very much the opportunity that 00:15:27.720 --> 00:15:32.037 everyone has in front of them is you have almost an AGI, maybe soon truly an AGI 00:15:32.120 --> 00:15:35.077 in your pocket. Can do anything you want. 00:15:35.160 --> 00:15:37.237 What is it you want? Waiting on you. 00:15:37.320 --> 00:15:39.357 It's waiting on your direction, your guidance, your management. 00:15:39.440 --> 00:15:42.397 >> It would be nice if eventually we can get some time back from AI. 00:15:42.480 --> 00:15:46.117 >> That's right. >> Like right now, I do feel like it it will help you work more. 00:15:46.200 --> 00:15:49.957 >> That's right. And so I think that I think that that is that is exactly a 00:15:50.040 --> 00:15:53.757 next step, right? It's really moving from this like you're micromanaging this 00:15:53.840 --> 00:15:56.437 agent. You have to provide it with these tasks and you have to give the context 00:15:56.520 --> 00:15:59.797 cuz it just doesn't have it. Exactly. You want something that has 00:15:59.880 --> 00:16:03.837 much more like you think about like a really good assistant who really is 00:16:03.920 --> 00:16:07.237 plugged in and you've given it again, you have to build up trust with it, 00:16:07.320 --> 00:16:09.917 right? You have to really say like, okay, I trust you with this context, I 00:16:10.000 --> 00:16:13.717 trust you with this responsibility, I trust you with this job, whatever it is. 00:16:13.800 --> 00:16:17.157 And once it's there, it should be able to proactively run and really execute. 00:16:17.240 --> 00:16:20.517 You wake up in the morning, you know, it could say, "Hey, here are five things I 00:16:20.600 --> 00:16:23.757 need from you to unblock me. You know, I just need approval on this. Like here's 00:16:23.840 --> 00:16:26.197 a question I have." Those kinds of things. As you drink your morning 00:16:26.280 --> 00:16:28.357 coffee, you say yes, no, yes, no. 00:16:28.440 --> 00:16:31.717 Then you go, come back, you know, late at night, and again you've got five more 00:16:31.800 --> 00:16:35.317 things. And so I think that it's going to be a world where it's going to be 00:16:35.400 --> 00:16:38.917 different from I think the kind of AGI world that many of us would have 00:16:39.000 --> 00:16:43.277 pictured 5 10 years ago. >> Software development may simply be the 00:16:43.360 --> 00:16:45.837 first profession showing us what this 00:16:45.920 --> 00:16:47.757 transition looks like. 00:16:47.840 --> 00:16:50.597 If similar agents move into this area, 00:16:50.680 --> 00:16:53.277 finance, marketing, design, and 00:16:53.360 --> 00:16:55.557 everyday's business world, the biggest 00:16:55.640 --> 00:17:00.917 question may no longer be what AI can do, but what humans choose to ask it to 00:17:01.000 --> 00:17:02.197 do. 00:17:02.280 --> 00:17:05.517 So perhaps AGI won't arrive with an 00:17:05.600 --> 00:17:08.157 announcement. We may simply look back 00:17:08.240 --> 00:17:13.360 and realize that the transition had already begun. 00:17:20.840 --> 00:17:22.880 >> Mhm.