WEBVTT 00:00:00.000 --> 00:00:02.120 Imagine you're standing at a crossroads, a digital 00:00:02.120 --> 00:00:05.259 one. Two really powerful paths stretch out before 00:00:05.259 --> 00:00:08.900 you in this evolving world of AI. One path promises 00:00:08.900 --> 00:00:11.640 just incredible speed, effortless access for 00:00:11.640 --> 00:00:14.339 pretty much everyone. The other offers, well, 00:00:14.839 --> 00:00:17.660 profound, deliberate reasoning. For the really 00:00:17.660 --> 00:00:20.359 tough challenges, how do you choose? It's not 00:00:20.359 --> 00:00:23.179 really about which one is better, is it? It's 00:00:23.179 --> 00:00:25.000 more about which one is right for what you need 00:00:25.000 --> 00:00:28.469 right now. Welcome to the deep dive. Today, we're 00:00:28.469 --> 00:00:30.629 unpacking that very challenge, navigating the 00:00:30.629 --> 00:00:34.570 choice between OpenAI's GPT -4 .0 and the newer 00:00:34.570 --> 00:00:36.990 GPT -5. It's a critical decision, really, shaping 00:00:36.990 --> 00:00:38.969 how we're all going to interact with artificial 00:00:38.969 --> 00:00:40.909 intelligence moving forward. Absolutely. And 00:00:40.909 --> 00:00:43.350 the launch of GPT -5, wow, it really sparked 00:00:43.350 --> 00:00:45.390 a debate, didn't it? I mean, you even had people 00:00:45.390 --> 00:00:47.229 saying they wish they could go back to GPT -4. 00:00:47.229 --> 00:00:49.390 That tells you something. It signals a shift, 00:00:49.429 --> 00:00:52.310 I think. We're moving beyond just raw power measurements, 00:00:52.409 --> 00:00:55.320 aren't we? Exactly. So it's less old versus new, 00:00:55.479 --> 00:00:59.399 and maybe more about two distinct design philosophies 00:00:59.399 --> 00:01:02.939 playing out. GPT -4 .0, the AI for everyone, 00:01:03.060 --> 00:01:05.500 kind of optimized for speed and cost. And then 00:01:05.500 --> 00:01:09.319 GPT -5, maybe the AI for experts, built specifically 00:01:09.319 --> 00:01:12.780 for that deep reasoning capability. That's the 00:01:12.780 --> 00:01:15.640 core of it. And look, we're not here today to 00:01:15.640 --> 00:01:18.200 give you a simple, use this one answer, that 00:01:18.200 --> 00:01:20.620 wouldn't really work. Our mission really is to 00:01:20.620 --> 00:01:22.719 give you a strategic framework, way to think 00:01:22.719 --> 00:01:26.000 about it. So we'll deconstruct their architectures 00:01:26.000 --> 00:01:27.980 a bit, look under the hood, we'll walk through 00:01:27.980 --> 00:01:29.900 some pretty rigorous real world tests that people 00:01:29.900 --> 00:01:31.939 have run, and then crucially connect it all back 00:01:31.939 --> 00:01:34.579 to how this AI stuff is gonna reshape the future 00:01:34.579 --> 00:01:36.859 of, well, your work. Okay, let's unpack this 00:01:36.859 --> 00:01:38.760 then. Starting with those design philosophies, 00:01:38.799 --> 00:01:40.799 what actually makes them different at a core 00:01:40.799 --> 00:01:43.489 level? To really get why they perform differently, 00:01:43.569 --> 00:01:44.989 you kind of have to look at how they're built, 00:01:45.030 --> 00:01:47.329 don't you? It's like understanding the engine, 00:01:47.689 --> 00:01:51.849 the chassis, the soul of the machine, so to speak. 00:01:52.189 --> 00:01:54.090 Yeah, let's start with GPT -4. You got a picture. 00:01:54.090 --> 00:01:56.629 It's like a Sprinter, a total masterpiece of 00:01:56.629 --> 00:01:58.849 optimization. Not really a completely new invention, 00:01:59.310 --> 00:02:02.629 but home to perfection. Its big thing is the 00:02:02.629 --> 00:02:05.629 unified architecture. What that means is it processes 00:02:05.629 --> 00:02:09.830 text, audio, images, all inside one single neural 00:02:09.830 --> 00:02:12.889 network. So where older models might pass data 00:02:12.889 --> 00:02:15.330 along like an assembly line, 4 .0 gets rid of 00:02:15.330 --> 00:02:17.449 that handoff. Think of it as omnipotent, maybe, 00:02:17.590 --> 00:02:20.009 but more in perception. It sees, hears, reads 00:02:20.009 --> 00:02:22.590 all at the same time in one brain. Imagine catching 00:02:22.590 --> 00:02:24.409 the sarcasm in your tone, not just the words 00:02:24.409 --> 00:02:26.370 you type, because it's getting all those inputs 00:02:26.370 --> 00:02:29.289 together. Sarcasm in tone. Wow, that actually 00:02:29.289 --> 00:02:30.590 feels like a pretty big leap. Does that mean 00:02:30.590 --> 00:02:32.810 we might finally get past those, you know, awkward 00:02:32.810 --> 00:02:34.590 AI moments where it just takes everything so 00:02:34.590 --> 00:02:37.189 literally? Well, potentially, yeah. Or at least 00:02:37.189 --> 00:02:40.719 closer. And to get that speed, that near -instant 00:02:40.719 --> 00:02:43.180 response, engineers probably use some clever 00:02:43.180 --> 00:02:46.879 tricks, like quantization that's basically rounding 00:02:46.879 --> 00:02:48.759 the model weights to make it smaller and faster. 00:02:49.039 --> 00:02:51.139 Think of it like making the numbers it uses simpler, 00:02:51.520 --> 00:02:54.199 and knowledge distillation. That's like teaching 00:02:54.199 --> 00:02:56.840 a small, quick model to act like a really big, 00:02:57.000 --> 00:02:59.680 smart one. This efficiency stuff is key. It drastically 00:02:59.680 --> 00:03:02.219 cuts the cost per query, which helps democratize 00:03:02.219 --> 00:03:04.060 AI, right? Makes it cheap enough for millions 00:03:04.060 --> 00:03:06.819 to use. So if I just need a fast, versatile AI 00:03:06.819 --> 00:03:09.400 for everyday stuff, 4 .0 is probably my go -to. 00:03:09.580 --> 00:03:12.580 Yeah, I'd say for that universal, reliable, quick 00:03:12.580 --> 00:03:15.740 assistance. Yeah. 4 .0 excels there. OK, so a 00:03:15.740 --> 00:03:19.039 4 .0 is the sprinter, GPT -5. Well, GPT -5 is 00:03:19.039 --> 00:03:21.379 more the contemplative thinker, a marathon runner 00:03:21.379 --> 00:03:23.699 maybe. This feels like a real architectural jump. 00:03:23.879 --> 00:03:26.300 It's designed not just to answer, but to reason. 00:03:26.490 --> 00:03:28.409 deeply. It seems to be built with something called 00:03:28.409 --> 00:03:31.530 dual system thinking, which is inspired by Daniel 00:03:31.530 --> 00:03:33.389 Kahneman's work, you know, thinking fast and 00:03:33.389 --> 00:03:35.530 slow. So it has a system one, a fast mode for 00:03:35.530 --> 00:03:37.389 the quick intuitive stuff, kind of like 4 -0. 00:03:37.590 --> 00:03:39.810 But the big deal is it also has a system two, 00:03:40.069 --> 00:03:42.930 a thinking mode. That one's slow, deliberate, 00:03:43.270 --> 00:03:45.960 logical. for the really complex problems. Okay, 00:03:46.099 --> 00:03:49.099 that thinking mode sounds, well, powerful, but 00:03:49.099 --> 00:03:51.460 does that mean it's always slower, even for simple 00:03:51.460 --> 00:03:53.719 things? Or can it, like, switch gears intelligently? 00:03:53.840 --> 00:03:56.240 How does it avoid getting, you know, bogged down 00:03:56.240 --> 00:03:58.219 in its own deep thoughts, for a quick question? 00:03:58.580 --> 00:04:00.319 Right, that's the clever part of the dual system. 00:04:00.840 --> 00:04:03.580 It should be able to switch. And inside that 00:04:03.580 --> 00:04:06.280 thinking mode, it uses some advanced techniques, 00:04:06.699 --> 00:04:09.620 like tree of thought. That means it explores 00:04:09.620 --> 00:04:11.780 multiple lines of reasoning at the same time, 00:04:11.919 --> 00:04:14.020 kind of like a chess grandmaster thinking several 00:04:14.020 --> 00:04:17.199 moves ahead. It's also got this self -critique 00:04:17.199 --> 00:04:19.839 mechanism. Think of it like an internal checks 00:04:19.839 --> 00:04:23.300 and balances. One part, the critic, refines what 00:04:23.300 --> 00:04:25.079 the other part, the generator, comes up with. 00:04:25.259 --> 00:04:27.699 Helps with quality, presumably. And then there's 00:04:27.699 --> 00:04:30.579 this pro mode people talk about. Probably... 00:04:30.319 --> 00:04:33.639 using a mixture of experts or MOE architecture. 00:04:33.939 --> 00:04:36.240 MOE basically means using lots of small specialized 00:04:36.240 --> 00:04:39.639 expert AIs inside the big one. It only activates 00:04:39.639 --> 00:04:42.079 the relevant experts for a task. That helps it 00:04:42.079 --> 00:04:44.879 know a lot about specific things without forgetting 00:04:44.879 --> 00:04:47.259 general stuff. They call that catastrophic forgetting. 00:04:47.680 --> 00:04:50.180 So GPT -5 is really engineered for problems that 00:04:50.180 --> 00:04:52.740 need like serious thought, not just quick answers 00:04:52.740 --> 00:04:55.379 off the top of its head. Precisely. It's fundamentally 00:04:55.379 --> 00:04:57.579 about reasoning and complex problem solving. 00:04:57.639 --> 00:04:59.860 That seems to be its core design principle. OK. 00:05:00.029 --> 00:05:01.889 Fascinating stuff on how they're built. Really 00:05:01.889 --> 00:05:04.490 different approaches. But the million -dollar 00:05:04.490 --> 00:05:06.910 question for you, listening, is probably, how 00:05:06.910 --> 00:05:08.569 does this actually play out in the real world? 00:05:08.649 --> 00:05:11.769 So let's put them through this 10 -round gauntlet 00:05:11.769 --> 00:05:13.810 of tests that folks have been running. All right, 00:05:13.810 --> 00:05:15.790 let's see where GPT -5 really pulls ahead, usually 00:05:15.790 --> 00:05:19.170 in that deep reasoning and strategy zone. So 00:05:19.170 --> 00:05:21.709 round one, web development and image analysis. 00:05:21.910 --> 00:05:24.009 This is interesting. GPT -5 acted more like a 00:05:24.009 --> 00:05:26.040 solution architect. It didn't just do what it 00:05:26.040 --> 00:05:28.319 was told. It proactively suggested adding an 00:05:28.319 --> 00:05:31.360 interactive ROI calculator to a web page, explaining 00:05:31.360 --> 00:05:34.199 why strategically for conversions. GBT -4 .0, 00:05:34.199 --> 00:05:36.160 on the other hand, was super competent, but more 00:05:36.160 --> 00:05:38.120 like a precise coder executing instructions. 00:05:38.839 --> 00:05:41.579 Then round five, creating a dashboard from sales 00:05:41.579 --> 00:05:44.129 data. Here, GPT -5 stepped up as kind of like 00:05:44.129 --> 00:05:46.430 a junior business analyst. It didn't just make 00:05:46.430 --> 00:05:49.110 charts. It interpreted trends, it formed hypotheses 00:05:49.110 --> 00:05:50.949 about why things were happening, and even made 00:05:50.949 --> 00:05:53.370 specific suggestions, like maybe shift marketing 00:05:53.370 --> 00:05:56.310 budget here. GPT -4 mostly just described what 00:05:56.310 --> 00:05:58.629 the charts showed. Accurate, but less analytical. 00:05:59.170 --> 00:06:01.550 Round six, fact checking and citations. This 00:06:01.550 --> 00:06:03.089 is a big one, right? Especially for research. 00:06:03.610 --> 00:06:06.149 GPT -5 showed significantly better accuracy here. 00:06:06.170 --> 00:06:08.069 It gave valid working links to research papers 00:06:08.069 --> 00:06:10.050 more often. I think the test showed only one 00:06:10.050 --> 00:06:12.810 broken link for GPT -5 versus 3 for 4 -0. And 00:06:12.810 --> 00:06:14.670 honestly, I still wrestle with prompt drift myself 00:06:14.670 --> 00:06:16.709 sometimes when trying to get good citations out 00:06:16.709 --> 00:06:18.189 of these things, you know, where the AI kind 00:06:18.189 --> 00:06:20.089 of forgets the specifics over a long chat. It's 00:06:20.089 --> 00:06:23.069 a huge challenge. So GPT -5 doing better suggests 00:06:23.069 --> 00:06:24.990 it might have some stronger internal grounding. 00:06:25.550 --> 00:06:28.649 And finally, round eight, advanced coding projects. 00:06:28.860 --> 00:06:31.459 GPT -5 didn't just spit out code, it gave a whole 00:06:31.459 --> 00:06:34.000 project structure, multiple files, clean architecture, 00:06:34.399 --> 00:06:38.680 like proper software development. GPT -4 .0 tended 00:06:38.680 --> 00:06:41.100 to give just a single block of code, more like 00:06:41.100 --> 00:06:43.079 writing code snippets than building a system. 00:06:43.300 --> 00:06:45.649 Okay, so it sounds like... for tasks needing 00:06:45.649 --> 00:06:47.850 that strategic thinking, that complex problem 00:06:47.850 --> 00:06:50.829 solving, maybe designing systems. Yeah. GPT -5 00:06:50.829 --> 00:06:53.389 really shines. Yeah, absolutely. It seems built 00:06:53.389 --> 00:06:55.529 for that kind of deep analysis and architectural 00:06:55.529 --> 00:06:59.029 design work. But let's flip it. Where does GPT 00:06:59.029 --> 00:07:02.029 -4 .0 win? Speed, precision, user experience 00:07:02.029 --> 00:07:05.269 seem to be its strengths. Round two, the raw 00:07:05.269 --> 00:07:07.610 speed test. 4 .0 was almost instant, like one 00:07:07.610 --> 00:07:10.769 to two seconds total response time. GPT -5, even 00:07:10.769 --> 00:07:13.139 in its faster mode. had a noticeable delay, maybe 00:07:13.139 --> 00:07:15.220 two to three seconds. Now, that might not sound 00:07:15.220 --> 00:07:16.860 like much, but for real -time stuff like chatbots, 00:07:16.980 --> 00:07:18.600 that difference is huge. It feels much more fluid 00:07:18.600 --> 00:07:21.860 with 4 .0. Then, round three, making a professional 00:07:21.860 --> 00:07:25.439 PDF document. This was Stark. GPT 4 .0 generated 00:07:25.439 --> 00:07:28.019 a... Basically, visually perfect PDF, business 00:07:28.019 --> 00:07:31.300 rating, looked great, GPT -5. The content was 00:07:31.300 --> 00:07:33.379 insightful, definitely. The formatting, it was 00:07:33.379 --> 00:07:35.680 described as a disaster, slight chuckle, text 00:07:35.680 --> 00:07:38.240 overflowing, weird headings, unusable visually. 00:07:38.459 --> 00:07:41.980 A visually perfect PDF from 4 .0 versus insightful, 00:07:42.439 --> 00:07:44.519 but let's say, artistically challenged formatting 00:07:44.519 --> 00:07:47.480 from GPT -5, slight chuckle. So I guess even 00:07:47.480 --> 00:07:49.360 AI genius can have trouble with layout sometimes, 00:07:49.399 --> 00:07:52.360 huh? Exactly. Like a brilliant professor whose 00:07:52.360 --> 00:07:55.199 office is just chaos. gets the ideas right, but 00:07:55.199 --> 00:07:57.959 the presentation needs work. It really highlights 00:07:57.959 --> 00:08:00.240 that these are tools with specific strong suits, 00:08:00.560 --> 00:08:04.279 not magic ones for everything. Okay, round nine, 00:08:04.939 --> 00:08:08.199 image generation and design. GPT -4 .0 seemed 00:08:08.199 --> 00:08:11.420 to prioritize constraint adherence. Meaning, 00:08:11.759 --> 00:08:14.019 it got the aspect ratio right, put text where 00:08:14.019 --> 00:08:16.220 it was asked to. Crucial for professional assets, 00:08:16.500 --> 00:08:18.899 right? Like logos or banners. GPT -5 was more 00:08:18.899 --> 00:08:20.720 artistic, maybe generated more stunning images 00:08:20.720 --> 00:08:23.060 sometimes, but often missed those technical specs. 00:08:23.519 --> 00:08:25.480 Wrong size, text cut off. This is a big old round 00:08:25.480 --> 00:08:28.579 ten. Memory in long -term context. Forro was 00:08:28.579 --> 00:08:30.939 the clear winner here. Someone revisited a conversation 00:08:30.939 --> 00:08:33.269 about planning a trip days later. GPT -4 will 00:08:33.269 --> 00:08:35.330 remember the context perfectly. Even the user's 00:08:35.330 --> 00:08:37.529 worries about the cold weather. GPT -5 seemed 00:08:37.529 --> 00:08:39.429 to have forgotten. It asked for clarification, 00:08:39.710 --> 00:08:41.690 like starting fresh. This is probably down to 00:08:41.690 --> 00:08:44.250 4 .0's very efficient RRaggitay system, retrieval 00:08:44.250 --> 00:08:46.049 augmented generation, which helps to pull relevant 00:08:46.049 --> 00:08:48.549 bits from past chats. So for those quick tasks, 00:08:48.649 --> 00:08:51.070 things needing visual precision or just natural 00:08:51.070 --> 00:08:53.809 conversation over time, it sounds like 4 .0 is 00:08:53.809 --> 00:08:57.070 still the leader for efficiency and just a smoother 00:08:57.070 --> 00:08:59.649 experience. Absolutely. For execution, speed, 00:08:59.750 --> 00:09:02.009 and accuracy on those kinds of tasks. really 00:09:02.009 --> 00:09:04.049 hard to beat right now. But it wasn't always 00:09:04.049 --> 00:09:06.250 one winning over the other. Sometimes they tied 00:09:06.250 --> 00:09:09.210 or just showed different styles. Round four. 00:09:09.789 --> 00:09:12.830 Extracting data from documents into JSON format. 00:09:13.409 --> 00:09:16.090 Structured data stuff. Both nailed it. Flawless 00:09:16.090 --> 00:09:19.250 performance. This seems to be table stakes now 00:09:19.250 --> 00:09:20.870 for top models. They just gotta be able to do 00:09:20.870 --> 00:09:24.470 this reliably. And round seven. Ideation and 00:09:24.470 --> 00:09:26.049 planning. This is interesting. Both were useful, 00:09:26.250 --> 00:09:29.690 but different. GPT -4 gave a very practical bottom 00:09:29.690 --> 00:09:32.330 -up. action plan, like step one, step two, step 00:09:32.330 --> 00:09:34.610 three. GBTI went top down. It generated more 00:09:34.610 --> 00:09:36.610 of a strategic framework, started with a hypothesis, 00:09:37.210 --> 00:09:39.870 defined success metrics, then outlined implementation. 00:09:40.169 --> 00:09:41.429 Both approaches are valuable, right? It just 00:09:41.429 --> 00:09:42.750 depends on what stage of a project you're in. 00:09:42.950 --> 00:09:45.389 Right. So for some core tasks, they're both really 00:09:45.389 --> 00:09:47.289 capable, but their underlying design kind of 00:09:47.289 --> 00:09:49.250 leads them to approach it differently. One practical, 00:09:49.429 --> 00:09:52.509 one strategic. Exactly. Both proficient, just 00:09:52.509 --> 00:09:54.309 bring different strengths, different styles to 00:09:54.309 --> 00:09:56.789 the table. Mid -roll sponsor read. OK, so we've 00:09:56.789 --> 00:09:58.549 looked under the hood. We've seen them go head 00:09:58.549 --> 00:10:01.230 to head in these tests. Fascinating stuff. But 00:10:01.230 --> 00:10:03.509 the really big question for you listening is, 00:10:04.570 --> 00:10:08.460 what does this all actually mean? For your work 00:10:08.460 --> 00:10:10.379 for the future because this isn't just about 00:10:10.379 --> 00:10:13.500 picking a slightly better tool. Is it this feels 00:10:13.500 --> 00:10:17.460 more like a Tectonic shift. It really is. Yeah, 00:10:17.500 --> 00:10:19.899 and here's the big takeaway. Maybe the bombshell 00:10:19.899 --> 00:10:22.539 these a eyes They aren't necessarily coming for 00:10:22.539 --> 00:10:26.100 your job, but they're coming to redefine it Massively. 00:10:26.320 --> 00:10:28.240 Forget just being the executor, the person doing 00:10:28.240 --> 00:10:30.980 the task. The future, I think, is about becoming 00:10:30.980 --> 00:10:34.080 the AI orchestrator, the supervisor, the strategist. 00:10:34.379 --> 00:10:36.039 That's where the human value is going to shift, 00:10:36.039 --> 00:10:39.019 and honestly, probably soar. From executor to 00:10:39.019 --> 00:10:41.039 strategist, that's a powerful idea. It really 00:10:41.039 --> 00:10:43.000 reframes how we should think about productivity. 00:10:43.179 --> 00:10:44.860 What does that look like, practically, for someone 00:10:44.860 --> 00:10:47.019 listening right now, in their role? Well, let's 00:10:47.019 --> 00:10:49.539 take some examples. Developers, they're going 00:10:49.539 --> 00:10:51.919 to be elevated, I think. Instead of writing tons 00:10:51.919 --> 00:10:54.519 of boilerplate code which... Frankly, GPT -4 00:10:54.519 --> 00:10:56.820 .0 can probably handle pretty well. They'll focus 00:10:56.820 --> 00:10:59.419 more on high -level architecture design and on 00:10:59.419 --> 00:11:02.200 supervising AI -generated solutions, maybe using 00:11:02.200 --> 00:11:05.740 GPT -5 to explore complex options. Data analysts. 00:11:06.059 --> 00:11:07.940 They'll move beyond just making basic charts. 00:11:08.139 --> 00:11:10.360 Their value will be in asking smarter business 00:11:10.360 --> 00:11:13.139 questions upfront and then interpreting the complex 00:11:13.139 --> 00:11:15.440 outputs, maybe from GPT -5, telling the story 00:11:15.440 --> 00:11:18.080 and the data. Content creators, marketers. GPT 00:11:18.080 --> 00:11:20.000 -4 .0 becomes their workhorse for daily stuff, 00:11:20.340 --> 00:11:23.220 emails, social posts, drafts. Fast and efficient. 00:11:23.340 --> 00:11:26.059 But they'll use GPT -5 as their strategy consultant, 00:11:26.480 --> 00:11:29.139 analyzing market reports, outlining whole campaign 00:11:29.139 --> 00:11:31.879 structures, finding deeper insights, even, say, 00:11:32.100 --> 00:11:35.159 lawyers or researchers. GPT -5 Pro mode, especially 00:11:35.159 --> 00:11:37.299 if those citation improvements hold up, could 00:11:37.299 --> 00:11:39.799 be a game changer for sifting through huge document 00:11:39.799 --> 00:11:42.080 sets or drafting initial arguments. And this 00:11:42.080 --> 00:11:44.000 isn't just individual roles, right? It ripples 00:11:44.000 --> 00:11:46.200 through the entire business process. Oh, absolutely. 00:11:46.279 --> 00:11:48.299 Think about R &D. You might use 4 .0 to quickly 00:11:48.299 --> 00:11:50.620 summarize, I know, 50 relevant research papers. 00:11:50.919 --> 00:11:53.120 But then you turn to GPT -5 to analyze across 00:11:53.120 --> 00:11:55.299 hundreds of sources and propose genuinely novel 00:11:55.299 --> 00:11:57.620 experimental directions based on identified gaps. 00:11:58.179 --> 00:12:00.820 Or sales, forward graphs, quick follow -up emails. 00:12:01.600 --> 00:12:04.360 But a sales manager uses GPT -5 to analyze CRM 00:12:04.360 --> 00:12:07.259 data for complex forecasts, spotting hidden risks 00:12:07.259 --> 00:12:09.519 or opportunities that a human might miss. Which 00:12:09.519 --> 00:12:11.659 brings us to a really crucial point for any business 00:12:11.659 --> 00:12:13.980 looking at this. How do you integrate these tools 00:12:13.980 --> 00:12:16.539 smartly and manage the cost? Because let's be 00:12:16.539 --> 00:12:19.379 real, a simple API called a GPT -40 might cost, 00:12:19.480 --> 00:12:21.580 what, a fraction of a cent? Super cheap. But 00:12:21.580 --> 00:12:24.039 a complex query hitting GPT -5's thinking mode? 00:12:24.159 --> 00:12:26.759 That could be 50, maybe even 100 times more expensive, 00:12:26.879 --> 00:12:29.059 because it uses so much more computation. So, 00:12:29.259 --> 00:12:31.679 the smart play. Build an internal AI router. 00:12:32.159 --> 00:12:34.019 Imagine a system that automatically looks at 00:12:34.019 --> 00:12:36.159 an incoming request and figures out how complex 00:12:36.159 --> 00:12:38.860 it is. Simple question. Repetitive task. Root 00:12:38.860 --> 00:12:41.779 it to the GPT -40 API. Low cost, high speed. 00:12:42.039 --> 00:12:44.820 Done. Complex problem. Needs deep analysis, strategic 00:12:44.820 --> 00:12:47.759 creativity. Route that to the GPT -5 API. Higher 00:12:47.759 --> 00:12:50.659 cost, but much higher value output. Whoa, just 00:12:50.659 --> 00:12:53.379 thinking about that. Imagine scaling that to 00:12:53.379 --> 00:12:57.210 like a billion queries a day efficiently. the 00:12:57.210 --> 00:12:59.230 cost savings and efficiency gains would be absolutely 00:12:59.230 --> 00:13:01.190 incredible. So essentially, you're building a 00:13:01.190 --> 00:13:03.649 smart traffic cop for AI requests, directing 00:13:03.649 --> 00:13:06.149 tasks to the most appropriate, and importantly, 00:13:06.450 --> 00:13:09.269 the most cost -effective model for the job. Precisely. 00:13:09.509 --> 00:13:11.710 It's all about intelligent resource allocation, 00:13:12.309 --> 00:13:14.769 using the right tool at the right cost for the 00:13:14.769 --> 00:13:18.570 right task at massive scale. Okay, so after diving 00:13:18.570 --> 00:13:21.330 this deep, it feels undeniably clear there isn't 00:13:21.330 --> 00:13:25.070 one simple answer to which model is better. there? 00:13:25.389 --> 00:13:27.049 The much more insightful question, the one you 00:13:27.049 --> 00:13:29.970 should be asking, is which tool is right for 00:13:29.970 --> 00:13:33.179 this specific task I need to do right now? Maybe 00:13:33.179 --> 00:13:36.139 think of GPT -4 .0 as that incredibly versatile 00:13:36.139 --> 00:13:39.320 Swiss army knife. It's fast. It's reliable. It's 00:13:39.320 --> 00:13:41.879 great for hundreds of everyday jobs. Always handy. 00:13:42.159 --> 00:13:44.440 Yeah, exactly. And GPT -5, that's more like your 00:13:44.440 --> 00:13:46.919 state -of -the -art R &D lab. It might be slower, 00:13:47.080 --> 00:13:49.019 definitely more expensive to run, but it's capable 00:13:49.019 --> 00:13:51.519 of those real breakthroughs, those profound insights 00:13:51.519 --> 00:13:53.519 that could change your whole strategy. So the 00:13:53.519 --> 00:13:56.200 wisest users, the most effective professionals 00:13:56.200 --> 00:13:59.149 moving forward. they'll become the conductor 00:13:59.149 --> 00:14:02.590 of their own AI orchestra, knowing instinctively 00:14:02.590 --> 00:14:05.549 when to call on the nimble, quick violin of GPT 00:14:05.549 --> 00:14:08.009 -4 -0 and when it's time to bring in the deep, 00:14:08.389 --> 00:14:11.669 powerful bass of GPT -5 for that truly profound 00:14:11.669 --> 00:14:14.309 impact. That's it. The future of productivity, 00:14:14.309 --> 00:14:16.870 I really believe, lies in this AI orchestration. 00:14:17.409 --> 00:14:19.129 It's becoming an art form, really, breaking down 00:14:19.129 --> 00:14:21.129 big problems, assigning the right pieces to the 00:14:21.129 --> 00:14:23.730 right AI model, maybe even multiple models working 00:14:23.730 --> 00:14:26.470 together, and then critically, synthesizing those 00:14:26.470 --> 00:14:28.389 outputs, weaving them together to create something 00:14:28.389 --> 00:14:30.549 more valuable than any single model could do 00:14:30.549 --> 00:14:33.129 alone, developing skills in prompt engineering 00:14:33.129 --> 00:14:35.129 of this broader orchestration. These are going 00:14:35.129 --> 00:14:37.590 to be crucial meta skills. So the call to action 00:14:37.590 --> 00:14:42.139 is clear. Start experimenting now. Today, build 00:14:42.139 --> 00:14:44.000 your own fluency with both of these incredible 00:14:44.000 --> 00:14:46.399 tools. Play with their strengths, understand 00:14:46.399 --> 00:14:48.100 their weaknesses, because the true power isn't 00:14:48.100 --> 00:14:50.519 just in the models themselves, it's in how you 00:14:50.519 --> 00:14:52.740 learn to combine them, how you conduct that orchestra. 00:14:53.200 --> 00:14:54.700 Thank you for diving deep with us today. It's 00:14:54.700 --> 00:14:56.879 a fascinating time to be watching this space 00:14:56.879 --> 00:14:59.159 evolve. Until next time, keep exploring, keep 00:14:59.159 --> 00:15:01.820 learning, and maybe most importantly, keep asking 00:15:01.820 --> 00:15:04.480 the right questions. O -T -R -O music.