Fable Is Here, But Is It Actually Better? | Invest with AI Vibe Check

Fable just shipped with the promise of being Anthropic’s most capable release yet. However, daily power users Brett Caughran (Fundamental Edge) and Khe Hy (Rad Reads) still can't conclude whether models have gotten meaningfully better in the last six months.

We run a round-the-horn vibe check: the eval where Fable read 10,000 minutes of Khe's calls and surfaced the one thing his business was avoiding, why complex Excel modeling still breaks, the Karpathy "knowledge wiki" for a 500-document deal folder, and the quiet case for Cowork over Claude Code.

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Timestamps:

[00:00] Intro
[00:57] — First Impressions of Fable (and Why the Ban Was a Relief)
[03:05] — The Eval That Found the Thing He Was Avoiding
[05:14] — Why Evals Are Hard, and the One-Shot Short Signal
[06:56] — The 100-Company Earnings Preview Test
[07:46] — The Bridgewater Judgment Layer (High-40s → Mid-70s)
[10:48] — Braintrust and Scoring MCP Vendors
[12:30] — The Karpathy Knowledge Wiki for 500-Doc Folders
[18:20] — Making Your File System Legible to Agents
[21:11] — Turning a 120-Page Manual into Skills
[22:04] — Thin vs. Thick Skills and Composable Subagents
[25:36] — Why Complex Excel Still Breaks (and the Subagent Fix)
[27:24] — Claude Code vs. Cowork: The Line Is Collapsing
[29:57] — The Infosec Case for Cowork (VM vs. Root Access)
[33:50] — Vibe Check: Skepticism Is Up, and "Bot-Sitting"
[36:26] — Digital Twins and Where the Buy-Side Actually Is

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Transcript

SPEAKER_01 0:00

So I gotta admit that I was excited when the government banned Fable out of the gates. I think they were all things that that like Opus probably could have done with a lot of steering, but Fable pretty much one-shot at them.

SPEAKER_00 0:13

Converting a 120-page operating manual on building a hedge fund style financial model is getting like quite a bit easier. Welcome to the next episode of Invest with AI, where we explore the intersection of fundamental investing and artificial intelligence. We have a short duo episode today where we'll do just a little bit of round-the-horn vibe check on AI and investing. And uh one of the key topics people are discussing these days uh is the new launch of Fable K. So, what uh what are the vibes on Fable by your assessment?

SPEAKER_01 0:56

Yeah, great to be here. Uh, great to see you. So I gotta admit that I was excited when the government banned Fable out of the gates, uh, not because I believe in any paternalistic intervention of any sorts, uh, but I was just like, shoot, I'm not ready. Uh and I think that you know, with all the hype on these like big expensive models, I gotta be honest with you, and I wonder how you approach this, is I don't have a great set of evals. And by evals, it is like, do I have a complex task or series of tasks with a lot of good context that is hard and that prior models have not fully succeeded at. And so I don't have like a predetermined set of evals, but I do have one thing that I run pretty regularly whether when every new model comes out. And that's basically I take I have a giant folder of like everything my business has ever done. So you're talking like 200 granola calls, 50 decks, MCP into my email, um different like surveys that people have filled out, the recordings of all my trainings, and I dump that in and I say build me a website, create a strategy uh document for me, or create a presentation from scratch just based on like all of these materials. And I gotta say, the result was pretty good. Like the website was beautiful, the deck was pretty good. Um but most importantly, the strategy doc, because that's where you know I'm intimately connected with my business, like I know it better than anyone else. So like if someone's gonna give me a unique insight, I I can, I'm like, that's a unique insight. And I'll point here, and again, this is not the most robust eval, but what I'll say is I had a call, a prospecting call with someone that was not even really a prospect, just kind of like half networking, half biz dev. But they said something to me that I was like, ooh, that's very that's like I feel seen. Like you you saw something that I didn't see in my own business. And it's one of those things that's like a little uncomfortable where like where I know I should pursue it, but I'm not pursuing it. And there's like reasons why I'm not pursuing it. And it was just one like one minute inside probably 10,000 minutes of phone calls. And it jumped right on that one minute, and it's like, you should be pushing so much harder on this. And that was again, I don't want to overanalyze this, but I had forgotten that that was a very salient point when they made it. And it was once, it didn't happen five times, it happened once. And the fact that it read through 10,000 minutes of calls and was like, this is the thing you need to focus on, was pretty shocking to me. So that's what I'll say on the like formal eval side of things, or the closest thing I have with formal. Um, I had it build a lot of things from scratch. So I had it build like a tool that any client of mine can describe themselves in a multi-step process. Like I'm a fundamental investor, this is what I look at, this is my investment horizon. And then it generates like 20 skills that they can then copy and paste into Claude. And I had it use all the skills that I had ever created for any of my clients, but also comb like YouTube and GitHub and like any public repository. So that was pretty cool. I think they were all things that that like Opus probably could have done with a lot of steering, but Fable pretty much one-shotted them. Now, I can't tell you about the price because I'm on the subsidy still, so like it's hard to know like, was that like $500 worth of credits or $30? I don't know. Probably closer to $500 than $30, if I had to guess. Was it worth $500? Probably not. Was it worth $30? Absolutely.

SPEAKER_00 5:14

Very, very interesting. Um, I agree so far that like the eval is hard, like eval is hard. I have a few that I uh still in the pipeline to run, like simple things like the level two, you know, financial modeling tasks that are just comp you know complex and heretofore the the models haven't been able to accomplish. Uh so I'll sort of get to those. Been traveling a little bit. Um, but I was I was running one where I was trying to turn um uh the process to identify signals around good times to short an individual stock. And Fable uh did an amazing job of sort of going back into the history of good times to short that stock and created a really coherent, you know, uh pattern recognition engine that historically I would have had would have had to steer quite a bit. It would have taken me two or three iterations to say, hey, no, it does, you know, we're not worried about a goodwill impairment or gap earnings. These are sort of the things that ultimately are triggers to to stocks, and I may have to feed that into you know transcripts, etc., or my own research and uh it one-shot at it, which was uh which was a little bit scary. Sort of catch catching that same fundamental dynamic that you um you just mentioned, like uh materiality or taste, like what's the right, what's the white right, right, right, right, right, right word for it.

SPEAKER_01 6:40

Um yeah, it's like incisive judgment. Yeah, like haystack stuff.

SPEAKER_00 6:46

Yeah, yeah, which is it's it's wild. Like I guess we would need to bring some experts in to explain uh exactly how how they they cook that in. Um, but I am start I am launching a new eval, which is a real real life eval. I'm taking a hundred companies and I'm running a hundred earnings previews as earnings season starts next week, and I'm going to basically give Fable uh two tasks, uh forecast revenue EBITDA and EPS accurately, and accurately forecast whether stock's going to trade up or down on the earnings print. So that's a very tight sandbox of tight sandbox of judgment. Um, and there's no hot there's no hiding from that. I'm gonna keep uh maybe I'll maybe we'll talk about that, you know, the results once earnings season's over. I'll put some of that on Twitter, obviously. That's right. Um but I thought that'd be a fun, I was inspired. I think the talk in my network for last week was this uh Thinking Machines Bridgewater report, where uh uh the the researchers took a large language model, which had native judgment and financial tasks in the high 40s, overlaid financial sort of a judgment layer, financial expertise, and judgment tasks raise up to 75%, like the mid-70s. Um that wasn't the most academically rigorous report, but I think directionally was inspiring to a lot of people thinking about this combination of human judgment with large language model judgment, that the combination could drive better better results. Um, and so I've taken a lot of time to codify into my earnings preview process the frameworks of judgment that lead to my estimation of whether a stock will trade up or down, things like setups and expectations and bicep whisper and business momentum and guidance trajectory, etc. Um, and so I'm excited to unleash that, uh unleash that with the fable uh model in this exercise. Um not I'd be shocked if it does really well, um, but I want to just at least kick this off because then the iteration into version 2.0 and feeding that back, I think is a fun data set to start to start uh building.

SPEAKER_01 9:09

Can I ask? So I hadn't seen that paper. Um, is it are they fine when you say they're adding the judgment, are they fine-tuning the model with that? Or is it in a skill or in a prompt? How is that judgment being uh incorporated into the process?

SPEAKER_00 9:26

I don't know the specifics. Uh I read it if you know, read it sort of relatively quickly, and uh maybe we need to try and get one of the authors in. That would be it, that would be a fun conversation. Um but I think the the conclusion was they they took this sort of codified analytical judgment of the Bridgewater team and overlaid that on the large language model approach. Um, and the common combination, the combination was valuable. And sort of uh in a in a um in a in a very few way, I've started to see that, right? When I started to see, okay, take this, take this approach, almost train it how to think, codify in some of the setup patterns that you know I've traded in the past. It it's a quick study, like the models have been a quick study on those patterns and and following on the rails of those those uh that that pattern recognition power. There's been a few instances where I'm like, huh, that was like nailed it pretty, pretty, pretty accurately. Not in a scientific way, hence I want to start sort of doing this in a in a broader subset to see if did I just get lucky and the heads flip, you know, head flipped uh on the coin twice in a row. Um, so I'm inferring skill, which is why um my plan to do this in a more systematic way in the earnings prints coming up starting next week.

SPEAKER_01 10:48

That's cool. Uh one thing I'll flag, I know we advertise this prod podcast as not having the answers, but us just talking through what we're what's on our radars. And um someone uh who knows a lot more about LLMs than I do had mentioned something called, I think it's called braintrust.dev. And it's uh it's a platform for running evals. And he had actually suggested uh that that I do an exercise kind of like the one you're describing. I'm not I'm not close enough to fundamental equity and investing to to do that. Uh, but he had suggested that I do that using this platform. So just putting it on your radar to maybe check it out that that there's actually like a structured environment to run these evals, and we can run that.

SPEAKER_00 11:35

That's interesting. Um, yeah, that's one of the one of the areas in our work where we're really like I've done a I've done evals a lot on vibes, right? It's like you know, you try you you shoot you shoot you shoot off a free few workflows, you're like, ah, this looks good. But the problem is even when you shot off five things in Opus 4.8, like you kind of like sometimes it works, sometimes it doesn't. So it's very hard to scientifically measure vibes. What a late one of the one of the things we're trying to do with clients is create a more scientific eval set of the various MCP inputs, right? So who are the modeling MCP vendors? Let's create an eval set so we can more scientifically score some of those vendors on on accuracy, uh, accuracy and capability. Um so it's a uh I'd say that's like an in work in progress, work in work in progress on our on our side. But yeah, good call out on the brain trust. I'll take a take a look at that.

SPEAKER_01 12:29

Cool. On my end, I'm a little late to the game, but it's something that I'm having a lot of fun with. So we're both Carpathy, Andre Carpathi fanboys, and probably, you know, like he drops a banger of a tweet every six weeks, and the AI world just like realigns around this new idea. So maybe two or three tweet cycles ago, so that might be, you know, 18 weeks ago. He had something, he called it kind of this like self-updating wiki, like a knowledge wiki. And and I kind of didn't have time to focus on it when it came out, and I'll circle back to the wiki with a very common problem that many of my clients are having right now. And that problem is they've gotten really good at cowork, some are using code, you know. And so now they've got good prompts, they've got good skills, all that stuff. They've connected their MCPs. But let's say they've got an investment, and this case like a private equity fund, they have an investment and there's 500 documents in that folder. Let's say that's you know, the investment is uh, I don't know, sweet green, right? So they have this sweet green. I'm gonna assume assuming it was private. They point it to the sweet green folder, but they've got 500 documents in that folder. And it's not even organized, and so on. And so if they want to ask questions about it, they could be very tactical questions like, you know, how has the store same sales growth changed over quarter over quarter? Or they could be more qualitative questions like how should this thesis, how is this thesis being ship informed by inflation? Right? Or tariffs, right? So you point at core, but core can't go through 500 files, right? And so what it does is it kind of cherry picks it. And what it really does is it does like basically like a lot of like command F. So like let's say you're doing tariffs, it's gonna do like command F on your 500 files and be like tariff, command F, uh inflation, command F, and then it's gonna grab 17 files, pull them into context, and then try to answer your question. And it starts to break down more and more as you have more and more documents, more and more MCP. MCP breaks down too, uh, because the context windows are too small when you're dealing with like huge volumes of data. So that's the problem that people are starting to have. And it's even more complicated by the fact that there's PDFs, there's Excel files, there's you know 200-page, you know, uh credit agreements and so on. So here's where the Carpathi wiki comes in is that the way this works is it says we're not gonna, we're we're going to create a map of your information, an ontology of your information. You might hear that word, people throwing that word around. And so what it does is it basically, let's say, it takes the 500 files and one at a time, well, first it will convert it into Markdown, and then it will basically read it. And as it reads it, it kind of creates a summary at the top with tags and metadata, and then it creates like links of ideas inside the text that it just read. So if you have something like, I don't know, uh tariffs, right? It will just like find all the references to tariffs. Then you ingest the next file and it does the same thing, like with one exception, as it finds tariffs, it then looks in all the other files and says, are there any references to tariffs in the other files? And then they start to link. And you kind of have this is kind of like you could think of it as kind of like a private worldwide web, right? Like a little wiki wiki, right? And so what's cool about this is that the LLM is doing the ingestion. So like taking the file, creating the links, and then as you get new information, it just keeps updating it. So there's this like self-improving process. Now I'm gonna be honest with you, like again, it's another one of these things, like hard to know if it's working versus the naive approach, which is you know, just point it at 500 with fable. But that being said, I I know that some clients have done this, like, you know, maybe like a concentrated, like a PE fund that has like 15 investments. They're like, we're gonna go through this process for our 15 investments. We're not gonna look at the 200 we passed on, but we'll do the 15. We understand it well. Every analyst can kind of own their name in the wiki. It's not like your style of investing where the turnover, you know, you're talking six-year investment period, so you're not flipping names and there's not data you're responding to every second. It's a little bit different. But what you start to have is this knowledge graph that is much easier for an LLM to traverse without blowing a ton of tokens and by increasing the depth of accuracy. So early days, early days in my own exploration on it, I do have a few clients that like I don't want to say like I brought it to them, like they kind of came up with it. We like they came up with it themselves and we compared notes. You know, a lot of these, a lot of this is around this idea of like if you can get all your files neatly organized in Markdown with good naming, good metadata inside like a nice clean folder structure that the LLM can go in and read it much better. And so you're starting to see, in again, I encourage everyone or I discourage anyone from doing this, like, oh, we're gonna like do 20 years of data across this. Like, no, no, pick like five names and start, pick one name and start. And so that's starting to happen, and and I'll be able to report back on the progress. But I I have seen some green shoots that again, this is more in private equity, but it works.

SPEAKER_00 18:25

It's it's interesting. And I've heard more about the same concept of um just making your file system more legible to the agents. How are how are clients doing that? Is that a skill that co-work or codecs can go in and organize? Like that even gives me hives when I start to think about organize like letting codecs rip on my internal files to make them legible, or people just pressing the button and hoping for the best?

SPEAKER_01 18:54

Yeah, I mean, I think like any of these things, you start with a little pilot. And so maybe you've got, you know, the sweet green folder with 500 files, and you'll say, uh, find me all the financials and put them in the financials folder, and then give it a descriptive file name. And then you go in and you're like, did it do it? You know, and as the analyst, you know, you know, sometimes an Excel file is a client list. You're like, I hope it didn't take the client list and put it in the models folder, right? So you kind of have to do that testing, but it's it's very good. And so uh so I so then you can kind of make this a more robust process, like every time a PDF comes in, immediately turn it and convert it to Markdown. Right. That that I know a lot of groups are doing that. Um, and then add the metadata while you're at it. So it's actually quite powerful. The models are very good at doing that, or you could even do that with a sonnet model. You don't even need Opus for like the the tagging and the cleaning and the moving and so on. Now, when you want to do it in you know bigger batches or with more complexity to it, more one-shotting, you would use a bigger model. But I would say again, like kind of start small, but you I think you would be very surprised that you know this is the file, and again, it all comes back to having good skills. Like this is the naming convention that we use for our files, year, year, year, month, month, day, day, you know, a quarter, you know, if it's a financial this. Um, and then another advantage, another thing you do is like you literally have a document that's like these are how the folders are organized. So you think about the LM comes in, it reads this kind of index document, and it's you're asking a question that involves models, then it's like, oh, let me read this. And and this file says all of the models live in the financials folder, right? Uh, and so it just saves the model, it saves the agent a lot of work in like where where do the models live? You know, do I need to open this file?

SPEAKER_00 20:54

Yeah, yeah. That makes uh that makes sense. It's interesting. We I think we both have the same fundamental rule. I call it the Carpothy rule of wait for Andre to tweet something and then try and try and think through how it applies to our space. I I've used that same wiki, knowledge wiki concept to build skills architectures. And a lot of what I've been doing is is really like the brain dump of everything I know about a certain concept, putting that into as deep of a sort of uh knowledge pool as possible, and then taking that same knowledge graph concept to sort of to make it agent legible for skills creation. Uh, and that's been quite slick quite slick uh from a process perspective. It makes a lot of the hard part actually is like compiling, compiling the knowledge. Um but converting a, you know, for example, converting a 120 page operating manual on building a hedge fund style financial model, taking that raw data into actual an op operational skills architecture is getting like quite a bit easier if you take that interim. interim step. So that's been um that's that's that's been fun. One of the things I've been spending a lot of time on is thinking about just the the the um uh the stack of agents like what's the right like we sort of had this concept back last year like four to six pages is the right uh depth for a prompt right because you know a one page prompt was probably too thin not descriptive enough but an eight page prompt was just too much and anything in the middle the large language model would lose attention you know that I curious your perspective on this in skills building too sort of trying to dial in myself of like how thin is too thin or how thick is too thick for a skill and um one of the concepts I've been using is this sort of like raw primitives I'm gonna see single purpose skills single purpose agents that are composable into these orchestrated pipelines and the subagents like the ability of a ramp skill to go pull in a dozen different subagents individual skills into this workflow pipeline I'm getting some really interesting stuff out of that um a sort of compos composer composability uh process which has been pretty exciting to me so far. Exciting my wife will tell me I'm a nerd and I get excited about skill composition but it's a little bit of like a breakthrough on my side of uh you know consistency and um each of each individual piece is sort of doing a much better job at adhering to the individual skill whereas if I load everything into one skill it's like things get lost in trend things get lost in translation.

SPEAKER_01 23:43

And uh you know I haven't spent as much time on that my skills do tend to be pretty narrow just by default but I haven't like it's not been a design choice just kind of the way the puck landed um but one thing you'll notice I don't know if you've noticed this on Fable but it does a it's kind of doing a lot of the subagent work on your behalf even without skills. And so for example if you ask it to you know create a uh I don't know create a final memo you could see it's like uh I'm gonna create a bunch of different subagents to read these documents and then report back and one of the things that I'm just starting to realize on subagents is that subagents use their own context right so they ring fence their own context window so they're not polluting the context window of the larger thread. And so if you have these like very discrete agents driven by skills as you just described, you have this like really efficient kind of context gathering exercise that then extracts the key information up to the mother, you know, the orchestrator or the main thread uh that then can kind of reassemble it and uses like the heavy duty intel, you know, the fable the Opa Opa style intelligence. Now again this is more through observation than from things I've like consciously architected myself.

SPEAKER_00 25:11

But I know that people you know you know I was a big open claw person back in the day like people who were using open claw were thinking through this six months ago of like when do you need to close ring fence context when do you need to bleed context into the main chat like this is why I always love talking about open claw is because they were six months ahead of the conversation whether it worked or not right yeah yeah no it's interesting it um you know like complicated Excel modeling has been one of the areas that's been quite disappointing um with with AI and I've had to really chunk things into individual steps and for whatever reason AI in the Excel wrapper just doesn't listen to my skill it doesn't follow directions well. I asked a uh I asked a um a contact about that and they're working maybe we'll bring him on um his firm is working on a subagent approach and that's sort of like that's very interesting to me like if I could take a highly complex LBO model or hedge fund style model it's just too much like it's too context inefficient token inefficient today to do that in an AIXL system. But if you can spawn 30 different subagents one to go clean up the cash flow statement one to go build out the interest schedule and then you bring in a fable on top of that to assemble those analyses in um you know deploying deterministic code where it makes sense um obviously the proof is in the pudding but conceptually that feels like a really interesting engineering approach um approach to this this this problem and again I I don't know enough about Fable but it seems like it's trying to do all of that on the fly yeah which would be crazy yeah yeah exactly exactly like because we're like talking about customizing every sub agent but again we don't want to get you know um get in front of you know get in front of the story right or mislead but it's there are little breadcrumbs that it has the capability to do at least some of that it makes sense one one question I had um how are you seeing your clients think about the trade-offs between Claude Code and Claude Cowork which is really just a different harness like where do you find like what do you find of the pros and cons of that that that decision to be it's it's funny you say dude my clients freaking love co-work uh and co work I don't I like code but I'm kind of nerdy I like the weirdness about it um they love cowork and I'll give you uh so so and I teach code if people want to learn it but now even after I teach it they're like what's the difference between co work and there are some differences but the differences are actually like collapsing for knowledge work.

SPEAKER_01 28:13

Like they'd be hard to notice like for coders you would know the difference. Like a coder would never use coork but it's the blur the lines are being more blurred. So a few things that are pretty cool on on on course one is the there's this concept of living artifacts live artifacts where you can run a dashboard off of an Excel spreadsheet and as you change that Excel spreadsheet the dashboard changes which is like one of the many re one of the main reasons why people were using coork claude code anyway was to just create more interactive visualizations of data. So that like knocks out a problem. Another one is that claude coork is pretty aggressive in solving problems with code. So even like if it can't figure something out it might say like can I can I write a Python script to do that. And it it didn't used to do that or it felt more constrained in the past on that. So it's getting a little bit more aggressive with coork um the other reason why people like coork is the schedule tasks right that's kind of how you trigger agentic workflows um they're so clean to run in courcan use your mouse you could see the which ones ran which ones didn't want run which ones need approval when you run schedule tasks in code it just goes into this black hole like it's like an it's like a there's a markdown file that like did your thing run like you know and so the ease of the user interface. And then this was something I actually just learned the other day because you and I don't get too deep into the InfoSec side of things. We kind of assume that people come to us once they've cleared the InfoSec bar uh information security for those unfamiliar with the acronym but one thing I just recently learned is that so what Cloud CoWork does is it actually creates a virtual machine. So you can think of this like a disposable desktop and so it's actually really hard to blow up your computer because you're actually using a copy of your desktop and I don't know exactly which cloud it lives on but with cowork with cloud code you're actually giving it root access. So it's not a copy like this is cloud based copy it's the real shebang. It's your computer right and so what I've heard from IT departments is that especially if the gap is converging in what they can do, they're like I don't want to give you know Brett root access when he's not even a coder I just want him to be able to use the cool features of cowork in this like virtual machine environment that's much safer. So I didn't actually know this distinction until like a couple of days ago but even since then it's come up twice already.

SPEAKER_00 31:14

Interesting interesting um yeah and if you know if you if you can sort of get the same power of cloud code through the the cowork wrapper and as a few of these bells and whistles that's sort of causing people do you lose any of the flexibility like it do you have more flexibility in cloud code to build dashboards or is there anything that cowork is constraining constraining of yeah I for knowledge work per se no but if you want to like you know some some of the things people want to do is like build these like more complex data scrapers where you need like a heavier build of code I think that I I I could be wrong on this but basically in coding there you you can like there's these open source libraries that's like oh if I want to run a scraper there's like 5000 people have created scraper tools on GitHub open source and you could just go grab one.

SPEAKER_01 32:14

You don't have to like rebuild the scraper right and I don't think co work has the ability to grab those packages for the security reason. So I think the more you want to reuse like more traditional coding components then you'd want to shift to code but again you'd have to really have more of a coding use case than a research knowledge work use case.

SPEAKER_00 32:41

Yeah so the develop more developer minded person is going to use cloud code and the more just end user who's working with files and building you know PDFs and uh Excel spreadsheets Excel spreadsheets is more living in cloud code cloud co-work but the distinction distinction is is compressing anyways from a computer is definitely compressing.

SPEAKER_01 33:06

Yeah and I've got some pretty technical clients that know how to use code and they're just like I can't be bothered I just use cowork it just it it does what I need it to do. And I to be honest I I'm falling more and more in that category like let me use my mouse more show me more things on the screen um and then just today they announced that they're gonna improve the mobile capability of co-work and so like the scheduled tasks used to only run if your computer was on.

SPEAKER_00 33:37

Yeah and apparently now they're gonna run if your like laptops off so they're moving some of the those powers to the cloud so that the announce it's not out yet but they announced it this morning oh interesting interesting okay great uh any last uh any last thoughts on on on VideCheck like what are how are people feeling about ai adoption and and finance right now I think I I feel like I'm encountering a little bit more I wouldn't say frustration but I think the skepticism level has gone up like 15% in the past quarter. Interesting.

SPEAKER_01 34:21

What are the pushbacks yeah sorry what are the pushbacks on that I think one is the we're spending so much time babysitting the AI when we could have just done it ourselves. That's one and I feel like after 18 months or so they're like oh we thought we would have like kind of left that period but we're still in it. I don't know if you saw Glean have this report called bots they call it bot sitting it's the invisible work and just like updating your context like checking for hallucinations reprompting you know I think they it's like I don't know it's like 30% of your AI time is bot sitting right so you're actually only 70% more effective. So I think there's a little bit of that and and then the cost the cost side of things just like is this worth the money and I think you know you're definitely seeing token budgets you know co-work is great. It uses a lot of tokens I don't think it's I think to make it so friendly to non-coders it probably you know I saw the analogy is probably uh taking a blowtorch to light a cigarette you know a few times um so there's definitely uh a little bit of that and and I think a few people have said look like they're like look if I'm really honest I didn't notice a difference between Opus 4.5 4.6 4.8 I'm not sure if I'm gonna notice a difference with Fable. I'm not sure if that's like a user error and I put myself in that category as well like I'm not pushing this hard enough or it's the reality that there is some kind of plateau for the types of things that we're doing right synthesizing documents writing reports analyzing data that's an open I no one's issuing a verdict on that but there's a little bit more like I'm really honest I don't know if it's really got if AI like the models have gotten that much better in the past three months six months.

SPEAKER_00 36:26

Yeah yeah it's interesting I think um you know the the the the public market like you know most of the clients we work with are you know scaled public market investors that have a tight coverage area and so chatbots were just like did not hit you know effective product market fit for that they're great for generalist firms that want to get up to speed and do you know research quickly. So they're probably later on that curve than some of your clients who are more in the private market space have been at this a little bit a little bit um longer this concept of a digital twin sort of overlaying the entire process like a digital analyst uh is something that we've been we've been scoping um and really was a little bit science fiction until it's very very very recently um so the vibes you know the vibes on those conversations I think have gotten quite exciting and maybe that hits the same wall at some point yeah um it's easy to have hope and and build prototypes it's another to actually deploy these things to to uh the effect of better decision making um yeah that's that's the hill we will we will all uh work to climb and hopefully bring in some some more guests to uh help inform that inform that climb yeah I'm excited this is a good vibe check good vibe check uh yeah great great great to connect Kay and uh we will be back uh we'll be back uh next uh next week with another guest and maybe next month with another vibe check we'll we'll uh we'll sort of monitor the vibes and bring those vibes back uh back to you so thanks everyone and let us know in the comments yeah let us know in the comments if what you think of these vibe checks what kind of things you want us to cover in future ones sounds great sounds great all right see everyone soon