WEBVTT 00:00:00.000 --> 00:00:02.379 We're all chasing speed with AI, right? Faster 00:00:02.379 --> 00:00:04.940 code, quicker insights, instant drafts. But what 00:00:04.940 --> 00:00:07.139 if all that efficiency is just leading us completely 00:00:07.139 --> 00:00:10.839 astray? Yeah, exactly. Imagine hitting the gas 00:00:10.839 --> 00:00:12.539 pedal harder, but you've totally forgotten the 00:00:12.539 --> 00:00:14.039 map. That's kind of where a lot of us are headed, 00:00:14.039 --> 00:00:16.480 it feels like. Exactly. So today we're asking, 00:00:17.160 --> 00:00:19.780 is AI really helping us make better decisions 00:00:19.780 --> 00:00:23.059 or maybe just bad decisions faster? Beep beep. 00:00:23.079 --> 00:00:25.019 Welcome back to the deep dive. This is where 00:00:25.019 --> 00:00:27.940 we unpack complex ideas, hoping to give you those 00:00:27.940 --> 00:00:30.960 aha moments. And I'm your co -host. For this 00:00:30.960 --> 00:00:33.140 deep dive, we're tackling a really fascinating 00:00:33.140 --> 00:00:35.619 piece of content asking, you know, how do we 00:00:35.820 --> 00:00:38.960 solve the right problem in the AI era. Yeah, 00:00:38.960 --> 00:00:41.280 we've got a stack of insights here today, and 00:00:41.280 --> 00:00:44.700 they all seem to point to one core idea. Knowing 00:00:44.700 --> 00:00:46.820 what problem to solve is now way more critical 00:00:46.820 --> 00:00:48.939 than how fast you can solve it. So we're going 00:00:48.939 --> 00:00:51.979 to explore this seductive trap of AI productivity, 00:00:52.479 --> 00:00:55.119 the hidden dangers, and then crucially how tools 00:00:55.119 --> 00:00:57.359 like design thinking can act as a sort of compass. 00:00:57.780 --> 00:00:59.280 Right. The goal is for you to walk away with 00:00:59.280 --> 00:01:02.280 a fresh perspective on how to really leverage 00:01:02.280 --> 00:01:05.659 AI. Not just as a tool for speed, but like as 00:01:05.659 --> 00:01:09.439 a partner in strategic thinking. Okay, let's 00:01:09.439 --> 00:01:12.280 dive in. There's this huge buzz around AI productivity 00:01:12.280 --> 00:01:14.640 hacks, right? Everyone's learning prompt engineering, 00:01:14.739 --> 00:01:16.780 trying to build faster. It feels like staying 00:01:16.780 --> 00:01:19.000 relevant just means executing quicker and quicker, 00:01:19.060 --> 00:01:22.239 but this analogy we found is pretty potent. It's 00:01:22.239 --> 00:01:24.599 like having this super powerful gas pedal, but 00:01:24.599 --> 00:01:27.489 no steering wheel. Right, and it's a... It's 00:01:27.489 --> 00:01:29.370 intoxicating, isn't it? That feeling of speed, 00:01:29.390 --> 00:01:32.450 teams are churning out analyses, presentations, 00:01:32.730 --> 00:01:35.409 automating workflows in hours, not days, things 00:01:35.409 --> 00:01:37.489 that used to take forever. But the catch, as 00:01:37.489 --> 00:01:39.590 the source points out, is they're often more 00:01:39.590 --> 00:01:42.510 lost than ever because speed without direction, 00:01:42.629 --> 00:01:44.870 that's just expensive wandering. It really shifts 00:01:44.870 --> 00:01:47.769 the key question from can we build this to should 00:01:47.769 --> 00:01:51.109 we even build this to sex silence? So why is 00:01:51.109 --> 00:01:53.769 optimizing purely for speed without that direction? 00:01:54.150 --> 00:01:56.650 Why is that such a dangerous path specific? now 00:01:56.650 --> 00:01:59.450 in the AI era? Well, because fast execution of 00:01:59.450 --> 00:02:01.750 the wrong idea leads to crashing harder, not 00:02:01.750 --> 00:02:03.909 reaching your actual destination. OK, let's unpack 00:02:03.909 --> 00:02:06.069 the real risks here then. The source material 00:02:06.069 --> 00:02:10.189 outlines three deadly dangers. Yeah, let's break 00:02:10.189 --> 00:02:12.689 them down. The first one, AI helps us create 00:02:12.689 --> 00:02:14.770 more solutions for problems that don't actually 00:02:14.770 --> 00:02:18.050 exist. How so? Well, AI democratizes solution 00:02:18.050 --> 00:02:20.069 building. Sounds great on the surface, right? 00:02:20.949 --> 00:02:25.780 But honestly, most people are not great at identifying 00:02:25.780 --> 00:02:28.699 real problems worth solving. So the result, you 00:02:28.699 --> 00:02:31.360 get these elaborate, technically impressive solutions 00:02:31.360 --> 00:02:34.159 for imaginary issues, built -in record time, 00:02:34.520 --> 00:02:36.639 beat. Think of it like a digital graveyard, just 00:02:36.639 --> 00:02:38.759 filling up faster with unused apps and features. 00:02:39.000 --> 00:02:42.300 OK, that makes sense. Danger number two, amplifying 00:02:42.300 --> 00:02:45.560 preexisting biases. This one's fascinating. The 00:02:45.560 --> 00:02:47.479 idea is AI doesn't make you a better problem 00:02:47.479 --> 00:02:49.439 finder. It just makes you a faster implementer 00:02:49.439 --> 00:02:51.479 of whatever you already assume. Exactly. Like 00:02:51.479 --> 00:02:53.419 the example given, if you think low engagement 00:02:53.419 --> 00:02:56.259 means you just need more content, AI is brilliant 00:02:56.259 --> 00:02:58.139 at helping you generate endless content. But 00:02:58.139 --> 00:02:59.800 it won't tap you on the shoulder and ask, hey, 00:02:59.900 --> 00:03:01.800 are you sure content is the actual problem here? 00:03:02.110 --> 00:03:04.229 Yeah, it basically creates this echo chamber 00:03:04.229 --> 00:03:07.150 for our own biases. It validates them with huge 00:03:07.150 --> 00:03:09.270 amounts of output, making us feel even more certain 00:03:09.270 --> 00:03:11.750 we're right. It reinforces blind spots instead 00:03:11.750 --> 00:03:14.210 of challenging them. The machine gives you what 00:03:14.210 --> 00:03:16.349 you ask for, not necessarily what you should 00:03:16.349 --> 00:03:19.229 be asking for. In the third danger, creating 00:03:19.229 --> 00:03:21.849 an illusion of progress. I can see this one. 00:03:22.110 --> 00:03:24.330 Doing things faster just feels productive, doesn't 00:03:24.330 --> 00:03:27.409 it? We mistake all that motion for actual progress. 00:03:27.680 --> 00:03:30.780 Absolutely. Teams start measuring success by 00:03:30.780 --> 00:03:33.520 features shipped, lines of code written, articles 00:03:33.520 --> 00:03:36.360 published, not by whether user behavior actually 00:03:36.360 --> 00:03:38.319 changed or if the business outcome improved. 00:03:38.620 --> 00:03:41.060 beat and that's just a direct path to burn out 00:03:41.060 --> 00:03:43.900 and frankly wasted resources. I'd admit I still 00:03:43.900 --> 00:03:45.879 find myself getting caught up in that shipping 00:03:45.879 --> 00:03:48.520 faster mindset sometimes even when I know better 00:03:48.520 --> 00:03:50.639 intellectually. It's a really seductive trap 00:03:50.639 --> 00:03:52.680 you know. Oh absolutely it's easy to fall into. 00:03:53.120 --> 00:03:56.060 So how do these dangers when they combine how 00:03:56.060 --> 00:03:58.659 do they collectively undermine real innovation 00:03:58.659 --> 00:04:01.240 and genuine impact? Well they lead to very efficient 00:04:01.240 --> 00:04:04.159 work but on the wrong things. Creating solutions 00:04:04.159 --> 00:04:07.360 nobody truly needs all based on flawed assumptions. 00:04:07.949 --> 00:04:10.789 So if execution speed is becoming table stakes, 00:04:11.330 --> 00:04:13.569 the source makes this really compelling argument. 00:04:14.150 --> 00:04:16.310 Execution was maybe never the real bottleneck. 00:04:16.449 --> 00:04:19.649 Right. That's the kicker. Projects often fail 00:04:19.649 --> 00:04:22.910 because teams build absolutely perfect solutions. 00:04:23.930 --> 00:04:25.949 But for problems that just weren't worth solving 00:04:25.949 --> 00:04:28.509 in the first place, the real bottleneck, looking 00:04:28.509 --> 00:04:31.189 back, has always been figuring out what to build, 00:04:31.529 --> 00:04:34.110 not just how fast you can build it. AI just throws 00:04:34.110 --> 00:04:36.410 that into sharp relief. And the examples given 00:04:36.410 --> 00:04:38.910 were pretty clear. Instagram didn't just execute 00:04:38.910 --> 00:04:41.550 photo sharing better. They tapped into a deeper 00:04:41.550 --> 00:04:44.850 need for curated moments for sharing life highlights 00:04:44.850 --> 00:04:47.910 easily. And Tesla, they completely redefined 00:04:47.910 --> 00:04:49.750 the problem of car. It wasn't just transport. 00:04:49.810 --> 00:04:52.230 It became about tech, sustainability, a whole 00:04:52.230 --> 00:04:55.350 experience. Exactly. So when everyone can execute 00:04:55.350 --> 00:04:57.509 at roughly the same lightning speed, thanks to 00:04:57.509 --> 00:05:00.470 AI, the only sustainable advantage left is knowing 00:05:00.470 --> 00:05:03.250 what's truly worth executing. This makes problem 00:05:03.250 --> 00:05:05.699 finding incredibly powerful. powerful. It's the 00:05:05.699 --> 00:05:07.959 new strategic high ground. So if everyone has 00:05:07.959 --> 00:05:10.120 similar execution speed now, where does that 00:05:10.120 --> 00:05:12.439 competitive advantage truly lie? It's really 00:05:12.439 --> 00:05:15.180 in that unique ability to identify and define 00:05:15.180 --> 00:05:17.339 problems that others haven't even noticed yet. 00:05:18.019 --> 00:05:21.480 Mid -roll sponsor, read placeholder. Okay, so 00:05:21.480 --> 00:05:24.379 if problem -finding is the new superpower, how 00:05:24.379 --> 00:05:26.560 do we actually get better at it? How do we cultivate 00:05:26.560 --> 00:05:29.160 it? This is where design thinking comes in. The 00:05:29.160 --> 00:05:31.800 source describes it almost like a compass for 00:05:31.800 --> 00:05:35.860 navigating this chaotic AI era. Yes. Instead 00:05:35.860 --> 00:05:38.199 of trying to compete with AI on speed, which 00:05:38.199 --> 00:05:40.959 is a losing game, we partner with it. We use 00:05:40.959 --> 00:05:43.180 it to deepen our problem solving. And design 00:05:43.180 --> 00:05:45.319 thinking provides the framework for that partnership. 00:05:45.879 --> 00:05:47.620 It's the one thing AI really can't do for you, 00:05:47.819 --> 00:05:49.939 the human insight part. Right. I see the synergy 00:05:49.939 --> 00:05:52.639 there. AI can generate a million solutions, but 00:05:52.639 --> 00:05:54.779 design thinking helps to find the right problem 00:05:54.779 --> 00:05:57.319 to solve in the first place. AI can optimize 00:05:57.319 --> 00:05:59.339 for any given metric, but design thinking helps 00:05:59.339 --> 00:06:01.459 you choose the right metrics that actually matter. 00:06:02.639 --> 00:06:05.540 The future belongs to those who can ask those 00:06:05.540 --> 00:06:07.759 deeper questions, who can observe actual human 00:06:07.759 --> 00:06:10.040 behavior and then, you know, prototype their 00:06:10.040 --> 00:06:13.009 assumptions quickly to learn. So how do humans 00:06:13.009 --> 00:06:16.329 and AI effectively collaborate within this design 00:06:16.329 --> 00:06:18.189 thinking framework? What does that look like 00:06:18.189 --> 00:06:20.850 practically? Humans really lead on the empathy 00:06:20.850 --> 00:06:23.870 and the problem definition parts. AI then assists 00:06:23.870 --> 00:06:27.089 with data analysis, idea generation, and it massively 00:06:27.089 --> 00:06:29.730 accelerates prototyping and testing. Okay, let's 00:06:29.730 --> 00:06:32.370 break down that process. The source gives a pretty 00:06:32.370 --> 00:06:34.649 clear roadmap for this human -AI collaboration 00:06:34.649 --> 00:06:37.629 in each phase, starting with phase one, empathize. 00:06:37.930 --> 00:06:41.470 This is human -led. but AI assisted. Right. The 00:06:41.470 --> 00:06:44.209 goal here is deep user understanding, seeing 00:06:44.209 --> 00:06:46.870 the world through their eyes, not just our assumptions. 00:06:47.610 --> 00:06:50.910 And crucially, AI cannot truly empathize. It 00:06:50.910 --> 00:06:53.370 doesn't have lived experience. So the human role 00:06:53.370 --> 00:06:56.529 is vital. conducting interviews, ethnographic 00:06:56.529 --> 00:06:59.170 studies, actually watching people reading between 00:06:59.170 --> 00:07:01.930 the lines, catching nonverbal cues. And AIs role 00:07:01.930 --> 00:07:03.930 in this phase? It's like the world's best research 00:07:03.930 --> 00:07:06.389 assistant. It can transcribe hours of interviews, 00:07:06.810 --> 00:07:09.189 summarize them, analyze sentiment from like thousands 00:07:09.189 --> 00:07:11.589 of online reviews. It could spot weird patterns 00:07:11.589 --> 00:07:13.350 in data that might suggest where humans should 00:07:13.350 --> 00:07:15.970 dig deeper. It handles the scale so humans can 00:07:15.970 --> 00:07:18.740 focus on the depth. Okay, then phase two, define. 00:07:18.879 --> 00:07:21.220 This is where human insight is apparently paramount. 00:07:21.420 --> 00:07:23.180 Yeah, this is probably the most critical phase. 00:07:23.579 --> 00:07:25.660 It's where you synthesize all that empathetic 00:07:25.660 --> 00:07:28.579 understanding into a clear, actionable problem 00:07:28.579 --> 00:07:31.620 statement. The human role is asking, okay, based 00:07:31.620 --> 00:07:34.660 on all this, what is the real problem here? Using 00:07:34.660 --> 00:07:38.759 frameworks like jobs to be done, JTBD. What job 00:07:38.759 --> 00:07:40.759 is the user trying to get done? Right, focusing 00:07:40.759 --> 00:07:44.860 on the underlying need and AI's role here. It 00:07:44.860 --> 00:07:47.199 can help refine and explore that problem space, 00:07:47.699 --> 00:07:49.899 but only after humans have provided high -quality 00:07:49.899 --> 00:07:52.879 input those synthesized insights. It's garbage 00:07:52.879 --> 00:07:55.500 in, garbage out, otherwise. There's a big difference 00:07:55.500 --> 00:07:58.439 between a bad prompt like generate 10 new features 00:07:58.439 --> 00:08:00.970 and a good one. Yeah, the source had a great 00:08:00.970 --> 00:08:03.310 example of an offensive prompt. Based on these 00:08:03.310 --> 00:08:05.550 20 user interview transcripts, synthesize the 00:08:05.550 --> 00:08:07.550 top five jobs to be done users are trying to 00:08:07.550 --> 00:08:09.990 accomplish. And it asks for pains, gains, obstacles, 00:08:10.449 --> 00:08:12.449 really structuring the insight AI is working 00:08:12.449 --> 00:08:15.410 with. And those JTBD examples were things like, 00:08:15.529 --> 00:08:17.310 help me catch up fast, or help me plan tomorrow 00:08:17.310 --> 00:08:20.189 quickly, or let me micro -learn a skill, nuanced 00:08:20.189 --> 00:08:22.790 human needs that AI can help structure but not 00:08:22.790 --> 00:08:25.189 originate. Gotcha. So once the problem is solid, 00:08:25.449 --> 00:08:28.819 then AI gets to really shine. Yeah. ID8. Exactly. 00:08:28.920 --> 00:08:31.959 Now you unleash the beast. Humans set the constraints, 00:08:32.440 --> 00:08:35.299 the direction, and act as curators. And AI becomes 00:08:35.299 --> 00:08:38.200 this amazing creativity engine. Yeah. Generating 00:08:38.200 --> 00:08:41.039 hundreds, thousands of ideas in seconds. Using 00:08:41.039 --> 00:08:43.740 techniques like Stamper or brainstorming from 00:08:43.740 --> 00:08:45.379 different perspectives. Think like a logistics 00:08:45.379 --> 00:08:47.279 company. Think like a psychologist. Think like 00:08:47.279 --> 00:08:50.879 a game designer. Whoa. Imagine scaling that creative 00:08:50.879 --> 00:08:54.720 output to like a billion queries, exploring literally 00:08:54.720 --> 00:08:57.240 every angle. It's mind boggling potential, right? 00:08:57.379 --> 00:09:00.769 Yep. Then phase four, prototype, goal. Quick, 00:09:00.970 --> 00:09:03.370 cheap experiments to test assumptions. Human 00:09:03.370 --> 00:09:05.690 role. Decide what needs prototyping. Identify 00:09:05.690 --> 00:09:07.610 the single riskiest assumption you need to test 00:09:07.610 --> 00:09:10.289 first. And AI just dramatically speeds this up. 00:09:10.389 --> 00:09:12.730 It can turn napkin sketches into clickable wireframes, 00:09:13.289 --> 00:09:15.509 generate realistic placeholder content, maybe 00:09:15.509 --> 00:09:17.929 even write simple code for basic functional prototypes. 00:09:18.049 --> 00:09:20.129 It shrinks the learning cycle massively. Okay, 00:09:20.149 --> 00:09:22.509 and finally phase five, test. Put the prototype 00:09:22.509 --> 00:09:24.490 in front of real users. Human role again is key. 00:09:24.720 --> 00:09:27.139 Design the test scripts, observe users directly, 00:09:27.440 --> 00:09:30.299 ask those crucial why questions, interpret contradictory 00:09:30.299 --> 00:09:33.519 feedback, all the subtle stuff. While AI handles 00:09:33.519 --> 00:09:36.779 the grunt work, analyzing test results, processing 00:09:36.779 --> 00:09:39.600 screen recordings, summarizing quantitative feedback, 00:09:40.080 --> 00:09:42.500 it frees up human brain power for the deeper 00:09:42.500 --> 00:09:45.779 insights. So when you boil it all down, which 00:09:45.779 --> 00:09:49.679 phase truly relies most on that unique, non -replicable 00:09:49.679 --> 00:09:52.259 human insight? Definitely empathy and defining 00:09:52.259 --> 00:09:55.549 the right problem. Those are uniquely human tasks 00:09:55.549 --> 00:09:58.649 AI can assist with, but not lead. Okay, so this 00:09:58.649 --> 00:10:00.769 isn't some abstract theory. It's a learnable 00:10:00.769 --> 00:10:03.230 framework. The source material provides a practical 00:10:03.230 --> 00:10:05.350 toolkit to actually hone these problem -finding 00:10:05.350 --> 00:10:07.909 skills. Number one, start with empathy mapping. 00:10:08.129 --> 00:10:10.490 Yeah, literally map out what users think, feel, 00:10:10.730 --> 00:10:12.950 see, say, and do. And critically, their underlying 00:10:12.950 --> 00:10:15.029 pains and gains. Not what you think they think 00:10:15.029 --> 00:10:17.529 or feel, but based on actual observation and 00:10:17.529 --> 00:10:20.159 interviews. So many teams just skip this. Number 00:10:20.159 --> 00:10:22.259 two, practice problem laddering with the five 00:10:22.259 --> 00:10:25.100 Ys. For any problem you identify, just keep asking 00:10:25.100 --> 00:10:28.039 why five times. The real root cause, the deeper 00:10:28.039 --> 00:10:29.940 insight, usually pops out around the third or 00:10:29.940 --> 00:10:33.419 fourth Y. So powerful. That example from the 00:10:33.419 --> 00:10:36.779 source. User aren't using feature X. Asking why, 00:10:36.779 --> 00:10:39.200 why, why eventually leads you to something fundamental 00:10:39.200 --> 00:10:42.600 like we need to rethink the app's entire information 00:10:42.600 --> 00:10:45.600 architecture based on core user needs. Totally 00:10:45.600 --> 00:10:49.009 different level of problem. Third. Become a professional 00:10:49.009 --> 00:10:51.889 observer. Like literally spend time just watching 00:10:51.889 --> 00:10:54.210 people interact with the world or your product 00:10:54.210 --> 00:10:57.149 or whatever context is relevant. AI gives you 00:10:57.149 --> 00:10:59.970 the what from data, but observation helps you 00:10:59.970 --> 00:11:04.129 understand the why. And fourth, experiment with 00:11:04.129 --> 00:11:06.429 problem statements. Don't just settle on the 00:11:06.429 --> 00:11:09.210 first one. Write it five different ways. Use 00:11:09.210 --> 00:11:12.820 that, how might we? Yeah, instead of just, our 00:11:12.820 --> 00:11:15.500 problem is high churn, try reframing. How might 00:11:15.500 --> 00:11:18.120 we make a new user's first 30 days incredibly 00:11:18.120 --> 00:11:20.799 valuable and engaging? That reframing itself 00:11:20.799 --> 00:11:22.899 can spark totally new ideas. So if you had to 00:11:22.899 --> 00:11:25.080 pick just one tool for a listener to start with 00:11:25.080 --> 00:11:27.480 today right now, what would it be? Definitely 00:11:27.480 --> 00:11:29.860 empathy mapping. It forces that shift from your 00:11:29.860 --> 00:11:32.960 own assumptions to actual user needs. It's foundational, 00:11:32.960 --> 00:11:36.000 I think. Totally agree. And this whole discussion... 00:11:35.879 --> 00:11:38.559 It really points to a fundamental shift in what 00:11:38.559 --> 00:11:40.720 it means to be a professional these days. We're 00:11:40.720 --> 00:11:44.259 moving from being just executors to becoming 00:11:44.259 --> 00:11:47.620 orchestrators. I love the conductor analogy used 00:11:47.620 --> 00:11:49.539 in the source material. It puts it perfectly. 00:11:50.179 --> 00:11:53.320 You're the conductor. AI tools are your orchestra. 00:11:53.600 --> 00:11:55.980 You've got experts in writing, coding, analysis, 00:11:56.179 --> 00:11:58.539 whatever. Your job isn't to play the violin and 00:11:58.539 --> 00:12:00.559 the trumpet. It's to choose the right music. 00:12:00.679 --> 00:12:03.360 define the problem, interpret the score, set 00:12:03.360 --> 00:12:05.720 the strategic direction, lead the orchestra, 00:12:06.179 --> 00:12:08.740 guide the AI tools, maybe using prompts as your 00:12:08.740 --> 00:12:11.720 baton, and crucially, listen and adjust, interpret 00:12:11.720 --> 00:12:14.100 the results, and iterate. Yeah, that's it. The 00:12:14.100 --> 00:12:16.960 human moves from doer to strategic thinker, connector, 00:12:17.299 --> 00:12:19.759 curator of quality. It demands a different skill 00:12:19.759 --> 00:12:21.620 set, a different mindset. So what do you think 00:12:21.620 --> 00:12:23.639 is the biggest challenge for professionals in 00:12:23.639 --> 00:12:26.120 making that shift, from executor to orchestrator? 00:12:26.539 --> 00:12:29.679 Probably letting go. letting go of the ingrained 00:12:29.679 --> 00:12:32.139 urge to do everything yourself, and learning 00:12:32.139 --> 00:12:35.000 to trust the AI to handle the detailed execution 00:12:35.000 --> 00:12:37.960 effectively. It's a big mental shift. Okay, let's 00:12:37.960 --> 00:12:40.399 try to wrap this up. Today's deep dive has really 00:12:40.399 --> 00:12:42.580 shown us that the future isn't necessarily about 00:12:42.580 --> 00:12:44.500 running faster, it's much more about going in 00:12:44.500 --> 00:12:47.740 the right direction. Absolutely. The real competitive 00:12:47.740 --> 00:12:51.179 edge in this AI -powered world isn't just using 00:12:51.179 --> 00:12:54.480 AI, it's knowing how to use it to identify and 00:12:54.480 --> 00:12:57.399 solve problems that genuinely matter. It really 00:12:57.399 --> 00:12:59.779 is problem finding over solution building now. 00:13:00.019 --> 00:13:02.679 And we learn that AI can definitely make us faster, 00:13:02.899 --> 00:13:05.500 yeah, but design thinking is what ensures we're 00:13:05.500 --> 00:13:07.879 faster at doing the right things. It's about 00:13:07.879 --> 00:13:10.320 combining that unique human empathy and insight 00:13:10.320 --> 00:13:13.399 with AI's incredible analytical and generative 00:13:13.399 --> 00:13:16.019 power. It's the partnership. So the next time 00:13:16.019 --> 00:13:18.279 you find yourself reaching for an AI tool, mainly 00:13:18.279 --> 00:13:21.000 just to speed things up, Maybe pause for a second. 00:13:21.240 --> 00:13:23.940 Ask yourself, am I just pressing the gas or am 00:13:23.940 --> 00:13:25.799 I actually checking the map? Yeah, choose where 00:13:25.799 --> 00:13:28.480 you invest your skill development wisely. The 00:13:28.480 --> 00:13:31.220 teams, the individuals who master this approach, 00:13:31.480 --> 00:13:33.860 they won't just build faster, they'll build truly 00:13:33.860 --> 00:13:36.240 elegant solutions to problems other people haven't 00:13:36.240 --> 00:13:38.899 even noticed yet. And that's a real sustainable 00:13:38.899 --> 00:13:42.240 advantage. So the final thought for you, what 00:13:42.240 --> 00:13:44.399 problem will you choose to define more deeply 00:13:44.399 --> 00:13:46.840 today? Thanks for joining us for this deep dive. 00:13:47.039 --> 00:13:49.039 Until next time, keep digging deeper.