WEBVTT 00:00:00.000 --> 00:00:02.640 You know, the exact feeling. It is midnight. 00:00:03.160 --> 00:00:06.500 The office is completely dead. Oh, yeah. We have 00:00:06.500 --> 00:00:08.960 all been there. You sit there rubbing your eyes. 00:00:09.419 --> 00:00:11.960 You are physically exhausted. You are literally 00:00:11.960 --> 00:00:15.080 copy pasting numbers from a blurry PDF report. 00:00:15.220 --> 00:00:17.480 Right into Excel. That's right into Excel. And 00:00:17.480 --> 00:00:19.440 you're doing this just to fix a single slide 00:00:19.440 --> 00:00:21.820 for tomorrow's pitch. It is a painful reality. 00:00:22.039 --> 00:00:24.859 We have all stared at that glowing screen. It 00:00:24.859 --> 00:00:29.100 feels like endless grunt work. Welcome, learner. 00:00:29.469 --> 00:00:32.350 to today's Deep Dive. We are looking at something 00:00:32.350 --> 00:00:35.170 incredibly practical today. We're exploring AI 00:00:35.170 --> 00:00:38.469 tools for finance professionals. And, you know, 00:00:38.689 --> 00:00:41.590 these tools exist specifically to kill that midnight 00:00:41.590 --> 00:00:44.729 grunt work. But there is a massive trap here. 00:00:45.350 --> 00:00:47.369 The biggest mistake people make is expecting 00:00:47.369 --> 00:00:50.710 one single tool to do everything. They want one 00:00:50.710 --> 00:00:53.450 AI to handle research, modeling, and presentation 00:00:53.450 --> 00:00:56.179 slides. Which is impossible. Right. That approach 00:00:56.179 --> 00:00:58.460 always collapses under pressure. It really does. 00:00:58.520 --> 00:01:00.219 You can't just throw a Swiss army knife at a 00:01:00.219 --> 00:01:02.880 construction project. You need the exact right 00:01:02.880 --> 00:01:07.420 tool for the specific job to sex silence. So 00:01:07.420 --> 00:01:10.760 we are going to trace the exact sequence of a 00:01:10.760 --> 00:01:13.280 real finance workflow today. From start to finish. 00:01:13.379 --> 00:01:15.719 Right. We will start with the initial due diligence 00:01:15.719 --> 00:01:18.260 and research. Then we will pull the trapped data 00:01:18.260 --> 00:01:21.510 out. Next, we will model that data properly. 00:01:21.849 --> 00:01:23.849 And finally, we will turn it into a pitch -winning 00:01:23.849 --> 00:01:26.909 presentation. It is all about building a highly 00:01:26.909 --> 00:01:29.969 reliable sequential system. You really cannot 00:01:29.969 --> 00:01:32.290 skip any steps here. So let's start at the very 00:01:32.290 --> 00:01:34.400 beginning of that workflow. Before you can build 00:01:34.400 --> 00:01:36.939 a fancy valuation model, you need the raw facts. 00:01:37.040 --> 00:01:38.879 You have to start with the correct data source. 00:01:39.340 --> 00:01:41.879 Absolutely. And I see so many analysts trip up 00:01:41.879 --> 00:01:43.719 immediately on this very first step. Oh, all 00:01:43.719 --> 00:01:45.599 the time. They immediately open up a general 00:01:45.599 --> 00:01:48.299 chat bot. They type a prompt into chat GPT or 00:01:48.299 --> 00:01:50.819 Gemini. They ask for specific financial metrics 00:01:50.819 --> 00:01:54.000 on a public company. Which, you know, seems logical 00:01:54.000 --> 00:01:56.420 at first glance. It feels intuitive. But general 00:01:56.420 --> 00:01:58.680 chat bots fail miserably here. They are just 00:01:58.680 --> 00:02:00.840 pattern matching engines. They guess the next 00:02:00.840 --> 00:02:03.140 word based on language patterns. Which is dangerous. 00:02:03.940 --> 00:02:06.640 incredibly dangerous in finance. They are highly 00:02:06.640 --> 00:02:09.979 prone to hallucinating. And just to clarify that, 00:02:10.280 --> 00:02:12.979 hallucination is when the AI confidently invents 00:02:12.979 --> 00:02:15.819 fake numbers. Yes. It invents them because they 00:02:15.819 --> 00:02:19.340 look statistically probable. Right. Using a general 00:02:19.340 --> 00:02:22.259 AI for finance research is like asking a poet 00:02:22.259 --> 00:02:25.659 to do your taxes. It sounds beautiful, but the 00:02:25.659 --> 00:02:28.099 underlying math is entirely fictional. That is 00:02:28.099 --> 00:02:30.300 a brilliant way to put it. You need highly specialized 00:02:30.300 --> 00:02:33.259 research tools instead. Like what? Well, let's 00:02:33.259 --> 00:02:36.199 talk about Fintool. Fintool is built specifically 00:02:36.199 --> 00:02:39.280 for researching U .S. listed companies. It doesn't 00:02:39.280 --> 00:02:42.030 guess the next word. It pulls data directly from 00:02:42.030 --> 00:02:46.370 actual SEC filings, and it reads verbatim earnings 00:02:46.370 --> 00:02:48.810 call transcripts. But wait, let me push back 00:02:48.810 --> 00:02:51.990 on that a bit. SEC filings are notorious for 00:02:51.990 --> 00:02:54.430 heavy management speak. Oh, yeah. They bury the 00:02:54.430 --> 00:02:57.389 real story in massive footnotes. Does Fintool 00:02:57.389 --> 00:02:59.669 actually cut through that corporate spin? That 00:02:59.669 --> 00:03:01.349 is the beauty of it. It doesn't just summarize 00:03:01.349 --> 00:03:04.330 the spin. Because it indexes the actual transcripts 00:03:04.330 --> 00:03:06.590 and the filings together, you can cross -reference 00:03:06.590 --> 00:03:09.180 them. Oh, I see. You can ask Fintool to generate 00:03:09.180 --> 00:03:12.159 a structured investment thesis. It will explicitly 00:03:12.159 --> 00:03:14.479 separate the company's stated growth drivers 00:03:14.479 --> 00:03:17.159 from the buried risk factors. So it maps the 00:03:17.159 --> 00:03:19.639 narrative against the required disclosures. Exactly. 00:03:19.879 --> 00:03:22.780 You can even screen for specific insider buying 00:03:22.780 --> 00:03:25.860 behavior. Really? Yeah. You ask it to show recent 00:03:25.860 --> 00:03:29.139 executive stock purchases. It returns the exact 00:03:29.139 --> 00:03:31.639 purchase amounts and shows you the specific buyer 00:03:31.639 --> 00:03:34.599 roles. That is a massive time saver for initial 00:03:34.599 --> 00:03:37.710 screening. But Fintool does have a strict limitation. 00:03:38.009 --> 00:03:41.270 It focuses heavily on U .S. stocks. Beat what 00:03:41.270 --> 00:03:43.750 if your mandate covers European markets or emerging 00:03:43.750 --> 00:03:46.189 economies? Then you absolutely need AlphaSense. 00:03:46.490 --> 00:03:50.009 AlphaSense is the global enterprise -grade alternative. 00:03:50.270 --> 00:03:52.449 It doesn't just look at public filings. What 00:03:52.449 --> 00:03:54.659 else does it pull? It accesses proprietary broker 00:03:54.659 --> 00:03:57.360 research worldwide. It pulls in global news feeds 00:03:57.360 --> 00:03:59.719 and complex regulatory filings across different 00:03:59.719 --> 00:04:02.120 jurisdictions. So it handles the heavy international 00:04:02.120 --> 00:04:05.139 due diligence. Yeah. It is widely used by major 00:04:05.139 --> 00:04:07.500 investment banks. It is absolutely perfect for 00:04:07.500 --> 00:04:10.719 preparing complex pitch books. It uses natural 00:04:10.719 --> 00:04:14.060 language processing to scan massive volumes of 00:04:14.060 --> 00:04:17.660 multilingual documents in seconds. I have a probing 00:04:17.660 --> 00:04:19.899 question about that, though. Do these specialized 00:04:19.899 --> 00:04:23.120 tools actually eliminate human blind spots entirely? 00:04:23.360 --> 00:04:27.079 Not entirely, no. They are amazing filters. They 00:04:27.079 --> 00:04:29.519 narrow down a massive mountain of documents into 00:04:29.519 --> 00:04:32.720 a highly focused pile. But they do not replace 00:04:32.720 --> 00:04:35.480 your human judgment. Fintool cannot tell you 00:04:35.480 --> 00:04:38.379 which specific risk factor actually matters most. 00:04:38.639 --> 00:04:40.579 They hand you the right puzzle pieces, but you 00:04:40.579 --> 00:04:42.500 build the picture. Exactly. You still have to 00:04:42.500 --> 00:04:44.699 do the thinking. So you've got these pristine 00:04:44.699 --> 00:04:47.180 summaries and broker reports. You know what you 00:04:47.180 --> 00:04:49.899 want to analyze. But now you hit the classic 00:04:49.899 --> 00:04:53.379 analyst's wall. The data you need is trapped. 00:04:53.610 --> 00:04:56.730 It is always trapped inside locked, unselectable 00:04:56.730 --> 00:04:58.449 PDFs. Right. You need to get those numbers out. 00:04:58.629 --> 00:05:00.850 You definitely cannot afford to manually retype 00:05:00.850 --> 00:05:02.930 them at midnight. Never. This brings us to the 00:05:02.930 --> 00:05:04.769 extraction phase. This is where a specialized 00:05:04.769 --> 00:05:07.769 tool called Quadratic really shines. Quadratic 00:05:07.769 --> 00:05:09.910 looks exactly like a traditional spreadsheet 00:05:09.910 --> 00:05:13.730 interface, but it has a powerful spatial AI layer 00:05:13.730 --> 00:05:17.170 built directly into it. Wait, spatial AI? How 00:05:17.170 --> 00:05:19.509 does that actually work in practice? Well, spatial 00:05:19.509 --> 00:05:22.689 AI is an AI that reads the visual layout of a 00:05:22.689 --> 00:05:25.329 document. Traditional extraction tools just read 00:05:25.329 --> 00:05:27.829 text from left to right. Which gets messy. Right. 00:05:27.930 --> 00:05:30.870 They get confused by weird spacing. Quadratic 00:05:30.870 --> 00:05:33.230 actually looks at the visual bounding boxes of 00:05:33.230 --> 00:05:37.290 the document. Imagine you have a dense 80 page 00:05:37.290 --> 00:05:40.170 annual report. You only need the income statement. 00:05:40.750 --> 00:05:43.209 Normally you scroll forever trying to copy and 00:05:43.209 --> 00:05:45.410 format it. And the formatting always breaks when 00:05:45.410 --> 00:05:48.290 you paste it. Always. But with quadratic, you 00:05:48.290 --> 00:05:51.209 simply upload the PDF. Then you give it a natural 00:05:51.209 --> 00:05:53.990 language prompt. You say, extract the income 00:05:53.990 --> 00:05:56.649 statement into a table with years as columns. 00:05:56.870 --> 00:05:59.769 And it just spatially maps and pulls it. It pulls 00:05:59.769 --> 00:06:02.209 the table perfectly. It places it directly into 00:06:02.209 --> 00:06:05.230 your spreadsheet view. No manual copying, zero 00:06:05.230 --> 00:06:07.899 formatting repairs. Whoa. Imagine scaling to 00:06:07.899 --> 00:06:10.180 comparing three years of reports from two different 00:06:10.180 --> 00:06:12.459 companies instantly. It handles that kind of 00:06:12.459 --> 00:06:14.920 scaling easily. You can bring multiple disparate 00:06:14.920 --> 00:06:17.980 files into one single working view. That is incredible. 00:06:18.259 --> 00:06:20.600 It even has a live connection feature built in. 00:06:20.819 --> 00:06:23.160 You can connect an external transaction feed 00:06:23.160 --> 00:06:26.379 via API. It updates your spreadsheet automatically 00:06:26.379 --> 00:06:29.019 on a set schedule. That completely eliminates 00:06:29.019 --> 00:06:34.689 so much manual updating work. Okay. I feel like 00:06:34.689 --> 00:06:37.110 we have to issue a vital warning here. Yes, a 00:06:37.110 --> 00:06:39.970 massive non -negotiable warning. A strict human 00:06:39.970 --> 00:06:42.529 check is absolutely mandatory. Because the AI 00:06:42.529 --> 00:06:45.790 is reading visual structure, right? Yeah. Sometimes 00:06:45.790 --> 00:06:47.990 those column headers get completely mislabeled. 00:06:48.050 --> 00:06:50.350 I have seen extra dates suddenly appear from 00:06:50.350 --> 00:06:52.410 nowhere. Just because a page number looked like 00:06:52.410 --> 00:06:55.009 a year. Right, you get errors. The spatial mapping 00:06:55.009 --> 00:06:57.810 isn't flawless. No, it is not. You always have 00:06:57.810 --> 00:07:00.170 to verify the output against the original source 00:07:00.170 --> 00:07:03.040 document. So what is the biggest trap when that 00:07:03.040 --> 00:07:05.519 extracted data looks perfectly formatted? The 00:07:05.519 --> 00:07:08.399 biggest trap is consolidation logic. A company 00:07:08.399 --> 00:07:10.220 might group line items differently year over 00:07:10.220 --> 00:07:13.100 year. The extracted table looks pristine. But 00:07:13.100 --> 00:07:15.459 the underlying components of something like operating 00:07:15.459 --> 00:07:18.160 expenses might have completely changed. The AI 00:07:18.160 --> 00:07:21.040 just blindly copies the top level label. Beautiful 00:07:21.040 --> 00:07:23.819 tables can still hide dangerously grouped numbers. 00:07:24.319 --> 00:07:26.040 Spot on. You always have to read the footnotes 00:07:26.040 --> 00:07:28.160 yourself. So extraction gets the data out of 00:07:28.160 --> 00:07:31.009 the PDF and onto a grid. But the actual heavy 00:07:31.009 --> 00:07:33.089 lifting has to happen where finance actually 00:07:33.089 --> 00:07:36.029 lives. It has to happen inside Excel. Excel is 00:07:36.029 --> 00:07:38.649 not going anywhere. It is the absolute bedrock. 00:07:39.110 --> 00:07:41.709 But AI can significantly supercharge your workflow 00:07:41.709 --> 00:07:45.149 inside it. Let's start with Microsoft's Excel 00:07:45.149 --> 00:07:49.689 Copilot. Copilot is fantastic for messy logic 00:07:49.689 --> 00:07:52.769 -based data cleanup. Give us a concrete example 00:07:52.769 --> 00:07:56.160 of that messiness. Think about categorizing highly 00:07:56.160 --> 00:07:59.279 varied credit card transactions. You have a massive 00:07:59.279 --> 00:08:02.699 list of raw expenses, thousands of rows. The 00:08:02.699 --> 00:08:05.139 descriptions say coffee shop, ride hailing app, 00:08:05.319 --> 00:08:07.620 or software subscription. You need to strictly 00:08:07.620 --> 00:08:10.300 categorize them into food, transport, or office 00:08:10.300 --> 00:08:12.779 costs. A standard VLO cup formula completely 00:08:12.779 --> 00:08:15.500 fails there. It completely fails. The exact text 00:08:15.500 --> 00:08:18.360 strings just do not match. Flash Fill also fails 00:08:18.360 --> 00:08:20.459 because there isn't a strict syntactic pattern. 00:08:21.100 --> 00:08:24.579 But Copilot uses semantic vector mapping. mathematically 00:08:24.579 --> 00:08:27.560 interprets the actual meaning of words. Exactly. 00:08:27.819 --> 00:08:30.000 It understands the context. It knows a coffee 00:08:30.000 --> 00:08:33.120 shop is conceptually related to food. It interprets 00:08:33.120 --> 00:08:35.080 the underlying context. It doesn't just strictly 00:08:35.080 --> 00:08:38.100 match characters' beat. But what about actually 00:08:38.100 --> 00:08:41.070 building out financial models from scratch? For 00:08:41.070 --> 00:08:44.049 generating first draft models, Claude directly 00:08:44.049 --> 00:08:47.210 inside Excel is fantastic. You just feed it a 00:08:47.210 --> 00:08:50.190 highly specific prompt. Like what? For example, 00:08:50.409 --> 00:08:52.970 you type, build a loan schedule, 20 year mortgage 00:08:52.970 --> 00:08:56.830 at 6 .5 % on a $500 ,000 principle. And it just 00:08:56.830 --> 00:08:59.139 builds the whole architectural table. It does. 00:08:59.159 --> 00:09:02.080 It sets up the entire grid. It even automatically 00:09:02.080 --> 00:09:04.539 color codes your blue cells for hard inputs and 00:09:04.539 --> 00:09:07.080 your black cells for formulas. I have a vulnerable 00:09:07.080 --> 00:09:10.019 admission to make here. Oh. I still wrestle with 00:09:10.019 --> 00:09:13.179 trusting AI to build full schedules. I find myself 00:09:13.179 --> 00:09:16.639 checking row by row, just... painstakingly slow 00:09:16.639 --> 00:09:18.879 math checks. You absolutely should be checking. 00:09:19.159 --> 00:09:22.039 Claude has severe context window limits. Meaning 00:09:22.039 --> 00:09:24.840 how much text the AI remembers at once. Right. 00:09:25.080 --> 00:09:27.779 Because of those limits, it sometimes just randomly 00:09:27.779 --> 00:09:32.179 stops processing. A 240 -month amortization schedule 00:09:32.179 --> 00:09:35.039 might abruptly just end at row 10 for no reason. 00:09:35.139 --> 00:09:37.500 It just completely loses the thread. Exactly. 00:09:37.720 --> 00:09:40.120 It forgets what it was doing. That is why, for 00:09:40.120 --> 00:09:42.500 actual heavy modeling, you need to step up to 00:09:42.500 --> 00:09:45.759 TraceLite. TraceLite is an AI built purely for 00:09:45.759 --> 00:09:49.080 rigorous finance work. How does TraceLite handle 00:09:50.149 --> 00:09:53.330 Dynamic, complex scenarios. Let's say you are 00:09:53.330 --> 00:09:56.649 analyzing a startup. Their starting monthly recurring 00:09:56.649 --> 00:09:59.850 revenue is $50 ,000. You need to model three 00:09:59.850 --> 00:10:03.350 distinct scenarios. OK. The best case is 15 % 00:10:03.350 --> 00:10:06.529 monthly growth. The base case is 8%. The worst 00:10:06.529 --> 00:10:09.210 case is 3%. That is very standard forecasting 00:10:09.210 --> 00:10:12.090 stuff. You prompt TraceLite to build a dynamic 00:10:12.090 --> 00:10:14.669 12 -month profit and loss statement based on 00:10:14.669 --> 00:10:17.289 those parameters. It instantly builds it on a 00:10:17.289 --> 00:10:20.720 clean new sheet. Wow. adds a functional scenario 00:10:20.720 --> 00:10:23.120 toggle at the top. That is incredibly powerful, 00:10:23.320 --> 00:10:25.539 Bede, but there's a massive structural risk there, 00:10:25.600 --> 00:10:28.820 isn't there? A huge risk. TraceLite usually generates 00:10:28.820 --> 00:10:31.299 that scenario toggle as a simple free text cell. 00:10:31.600 --> 00:10:34.740 If a junior analyst types worst instead of worst 00:10:34.740 --> 00:10:37.519 case, the entire model breaks instantly. The 00:10:37.519 --> 00:10:39.879 formulas completely lose their specific string 00:10:39.879 --> 00:10:42.059 reference. Yeah, the reference errors cascade 00:10:42.059 --> 00:10:45.120 everywhere. A pro tip here is to immediately 00:10:45.120 --> 00:10:48.539 replace that free text AI cell. Use standard 00:10:48.539 --> 00:10:51.419 Excel data validation drop downs instead. It 00:10:51.419 --> 00:10:53.600 takes five seconds to fix and it completely saves 00:10:53.600 --> 00:10:55.720 your model from typos. I have noticed something 00:10:55.720 --> 00:10:58.759 though. Why do these AI tools completely fall 00:10:58.759 --> 00:11:01.980 apart on multi -sheet three -statement models. 00:11:02.320 --> 00:11:04.820 It comes down to AI working memory constraints. 00:11:05.740 --> 00:11:08.259 A proper three -statement model requires holding 00:11:08.259 --> 00:11:10.600 hundreds of highly interdependent variables in 00:11:10.600 --> 00:11:12.919 mind simultaneously. Right. It is like trying 00:11:12.919 --> 00:11:15.340 to play a complex game of chess while looking 00:11:15.340 --> 00:11:18.679 through a tiny straw. The AI loses track of the 00:11:18.679 --> 00:11:20.720 grand architecture across the different tabs. 00:11:21.080 --> 00:11:22.980 It forgets exactly how the balance sheet links 00:11:22.980 --> 00:11:25.259 back to the cash flow statement. AI is great 00:11:25.259 --> 00:11:28.309 at single tasks. bad at holding the whole building's 00:11:28.309 --> 00:11:31.509 blueprint. Exactly right. Keep the AI strictly 00:11:31.509 --> 00:11:35.110 focused on modular, single -sheet tasks. Mid 00:11:35.110 --> 00:11:38.649 -roll sponsor, Reed. We are back. We have thoroughly 00:11:38.649 --> 00:11:41.809 researched our initial data. We have safely extracted 00:11:41.809 --> 00:11:44.769 it from those stubborn PDFs. We have rigorously 00:11:44.769 --> 00:11:47.799 modeled it inside Excel. But raw spreadsheets 00:11:47.799 --> 00:11:50.559 do not actually win client pitches. No, they 00:11:50.559 --> 00:11:52.539 do not. You have to communicate those findings 00:11:52.539 --> 00:11:55.440 visually. Presentations are everything. And formatting 00:11:55.440 --> 00:11:59.120 slides manually is mind -numbingly tedious. It 00:11:59.120 --> 00:12:01.000 is the absolute worst part of the job. Which 00:12:01.000 --> 00:12:03.559 is precisely why Claude integrated directly into 00:12:03.559 --> 00:12:06.840 PowerPoint is so highly useful. It actively pulls 00:12:06.840 --> 00:12:09.500 your live Excel data directly into your presentation 00:12:09.500 --> 00:12:11.820 slides. How aggressively specific do you need 00:12:11.820 --> 00:12:13.639 to be with your prompts? You have to be incredibly 00:12:13.639 --> 00:12:15.559 specific. You give it a highly structured prompt 00:12:15.559 --> 00:12:18.570 like... Create a single page company profile 00:12:18.570 --> 00:12:21.990 for Apple using the ticker AAPL. Include a business 00:12:21.990 --> 00:12:24.490 overview, key financial metrics, and major risk 00:12:24.490 --> 00:12:26.669 factors. Does it actually handle the visual design 00:12:26.669 --> 00:12:30.389 layout itself? It does, but you absolutely must 00:12:30.389 --> 00:12:33.529 guide it. You need to explicitly specify a white 00:12:33.529 --> 00:12:36.090 background in your text prompt. You must demand 00:12:36.090 --> 00:12:39.409 a clean, minimalist layout. What happens if you 00:12:39.409 --> 00:12:41.429 don't? If you do not dictate the aesthetics, 00:12:41.769 --> 00:12:44.389 you will spend three hours manually fixing the 00:12:44.389 --> 00:12:46.929 hideous formatting it guesses. What if you don't 00:12:46.929 --> 00:12:49.230 have hard data yet? What if you need to build 00:12:49.230 --> 00:12:51.629 a high -level concept deck from scratch? Then 00:12:51.629 --> 00:12:54.690 you pivot and use Gamma. Gamma is an AI platform 00:12:54.690 --> 00:12:57.750 built purely for sheer visual speed. So you wouldn't 00:12:57.750 --> 00:13:00.309 use it for heavy data integration? Right. It 00:13:00.309 --> 00:13:02.990 is striply for communicating ideas and concepts. 00:13:03.309 --> 00:13:06.049 You tell the prompt engine to create a six -slide 00:13:06.049 --> 00:13:09.210 executive deck. Okay. Let's say comparing equity 00:13:09.210 --> 00:13:11.970 versus debt financing for a small logistics business, 00:13:12.529 --> 00:13:15.070 Gamma's engine instantly handles all the visual 00:13:15.070 --> 00:13:17.830 layouts automatically. It is absolutely perfect 00:13:17.830 --> 00:13:20.230 for rapid internal strategy decks. It provides 00:13:20.230 --> 00:13:22.169 that foundational visual structure instantly. 00:13:22.509 --> 00:13:24.649 Yeah. What about reporting decks that we have 00:13:24.649 --> 00:13:27.070 to painfully update every single month? For recurring 00:13:27.070 --> 00:13:29.210 monthly reviews, you really want to use Bricks. 00:13:29.470 --> 00:13:32.129 Bricks is designed to build interactive live 00:13:32.129 --> 00:13:34.539 updating dashboards. How does that work? The 00:13:34.539 --> 00:13:37.440 visual structure of the presentation stays exactly 00:13:37.440 --> 00:13:39.860 the same month over month, but the underlying 00:13:39.860 --> 00:13:42.740 numbers change dynamically as new data flows 00:13:42.740 --> 00:13:45.340 in. It securely connects your raw spreadsheet 00:13:45.340 --> 00:13:48.340 directly to the final visual output. Exactly. 00:13:48.500 --> 00:13:51.220 It is absolutely perfect for tedious monthly 00:13:51.220 --> 00:13:53.899 budget tracking or variance analysis. Is the 00:13:53.899 --> 00:13:56.519 hallucination risk higher when an AI is trying 00:13:56.519 --> 00:13:58.899 to make something look pretty? Absolutely it 00:13:58.899 --> 00:14:02.240 is. Design -focused AI models are explicitly 00:14:02.240 --> 00:14:04.919 optimized for visual layout, not mathematical 00:14:04.919 --> 00:14:08.559 accuracy. Wow. They will happily invent a plausible 00:14:08.559 --> 00:14:11.279 -looking bar chart or perfectly round a crewful 00:14:11.279 --> 00:14:13.919 number simply because it makes the slide look 00:14:13.919 --> 00:14:16.320 more symmetrical. Pretty slides can easily distract 00:14:16.320 --> 00:14:18.259 from completely fabricated data points. That 00:14:18.259 --> 00:14:20.700 is the ultimate danger. You cannot ever let your 00:14:20.700 --> 00:14:22.759 guard down just because a chart looks professionally 00:14:22.759 --> 00:14:26.460 designed. Let's move to our big idea recap. We 00:14:26.460 --> 00:14:28.679 have covered a massive amount of ground today. 00:14:29.179 --> 00:14:31.580 The overarching philosophy here is remarkably 00:14:31.580 --> 00:14:34.519 simple. You must match the specific tool to the 00:14:34.519 --> 00:14:37.700 specific step. Never, ever use one single tool 00:14:37.700 --> 00:14:40.600 for everything. Use Fintool or AlphaSense for 00:14:40.600 --> 00:14:43.399 your foundational research. Use Quadratic for 00:14:43.399 --> 00:14:46.399 your structured document extraction. Use Copilot, 00:14:46.820 --> 00:14:49.179 Claude or Tracelite, Inside Excel for modeling. 00:14:49.639 --> 00:14:52.080 And use Claude, Gamma or Bricks for your final 00:14:52.080 --> 00:14:55.100 presentation slides. And the absolute, undeniable 00:14:55.100 --> 00:14:58.500 golden rule. AI speed must always be paired with 00:14:58.500 --> 00:15:01.320 rigorous human review. Those review habits are 00:15:01.320 --> 00:15:03.639 completely non -negotiable. Always check three 00:15:03.639 --> 00:15:06.100 to five random line items on your extracted data 00:15:06.100 --> 00:15:08.840 directly against the source PDF. Yes. Always 00:15:08.840 --> 00:15:11.659 test your AI generated models with a deeply familiar 00:15:11.659 --> 00:15:14.629 known scenario to check the math. And, critically, 00:15:15.049 --> 00:15:17.649 read every single number on a presentation slide 00:15:17.649 --> 00:15:19.669 out loud before sharing it. Reading out loud 00:15:19.669 --> 00:15:21.809 catches so many hidden errors. It physically 00:15:21.809 --> 00:15:23.809 forces your brain to slow down. You actually 00:15:23.809 --> 00:15:25.929 process the math instead of just skimming the 00:15:25.929 --> 00:15:27.730 visual shapes. It really does. It breaks the 00:15:27.730 --> 00:15:30.570 visual hypnosis. Two -sex silence. So, learner, 00:15:31.070 --> 00:15:33.049 here's our specific call to action for you today. 00:15:33.529 --> 00:15:35.350 Take a hard look at your current weekly workflow. 00:15:35.789 --> 00:15:38.129 Identify the single most painfully time -consuming 00:15:38.129 --> 00:15:40.629 step. Just pick the absolute worst bottleneck. 00:15:40.779 --> 00:15:43.200 And try integrating just one of these specialized 00:15:43.200 --> 00:15:45.700 tools this week. See how it materially changes 00:15:45.700 --> 00:15:48.980 your workday. Start very small. Build your trust 00:15:48.980 --> 00:15:52.179 gradually. Yeah. Beat. I want to leave you with 00:15:52.179 --> 00:15:54.120 a final slightly philosophical thought. Let's 00:15:54.120 --> 00:15:57.419 hear it. If specialized AI eventually handles 00:15:57.419 --> 00:15:59.899 all the tedious grunt work of extracting PDF 00:15:59.899 --> 00:16:02.679 data and building those initial baseline models, 00:16:03.220 --> 00:16:05.159 does the future finance professional become less 00:16:05.159 --> 00:16:07.700 of a mathematical builder and more of an editor 00:16:07.700 --> 00:16:09.940 or a philosopher of risk? That is definitely 00:16:09.940 --> 00:16:11.700 something to think about as the industry shifts. 00:16:12.100 --> 00:16:13.799 Thanks for joining this deep dive.