00:32:37 · Luca Ferrari
[00:32:37] S2: The two game changers are one, we make them super fit for purpose and very specific to our needs and these tools are natively integrated with one another, so the sum of the parts is much greater than what you could possibly accomplish by buying these different tools from different vendors, because each is better for what we exactly need to do and they also work in a more harmonious way together. Also because we can... There are this big monolithic foundation, we can swap out the foundation at an acquired business quite rapidly. It's actually much faster to change the entire thing in one or more or less the entire thing in one shot than removing one piece and then you place the alternative and you need to plug it to everything else, then remove another piece, plug it to everything else. So actually swapping the entire thing out tends to be faster. Now of course this is not particularly relevant to a single product company because they they don't need to buy new companies so that's that's not what they need. But they could probably benefit from a bunch of these tools. A lot of the value is unlocked by having the right culture and the right talent because like every tool ultimately the user makes a ton of difference and so depending on what company we're talking about they could be able to take advantage of it or not but some are more let's say autonomous and they don't require a lot of effort and quite powerful for example we built something called ALT ALT, like alternative spooner, that's a... It's basically an agent that lives in Slack and you talk to an ass, automatically has access to all the same tools and assets as you do. It's the same GitHub repos, same folders drive and whatnot, and you can task it to do things for you. Anything you could do in theory could do, and increasingly it can do and does. So you can tell it, for example, to run a data analysis for you and it will go fetch the data. If it doesn't have access, it will escalate a request to our credentials management team and potentially our IT security team if relevant.
00:34:41 · Luca Ferrari, Anish Acharya
Get get the data process it show your graph and commentary you don't have to ask anybody else it you get it in five minutes say many man hours and certainly a lot of delay in the exchange of information I experienced an interesting situation where a general manager of Evernote for us in a channel where we post feedback on the product she wrote to her agent I've seen this bug can you please go to Moros which is our customer support tool to check whether other customers have reported it then go to the code base find a root cause code a fix and ping Marco which is our engineering lead on that product to review that fix and fix it and and it got done and that would I mean it it was a 100 times better than what was possible before. So that's maybe a tool that I think could be close to a game changer for a lot of companies. [00:35:32] S1: That sounds really powerful. As you think about the way models get deployed within the company, do you think there are any job functions where the model will be doing a 100% of the work in the near future, or do you imagine it being a kind of lever for individual productivity as you just described?
00:35:46 · Luca Ferrari
[00:35:46] S2: 100%. So a customer support, for example, we have evolved our... All of our customer support managers from being more of like agents answering, you know, responding to tickets. Mhmm. Now we are for that we pretty much a 100% use either AI or for the residual portion of the tickets external vendors and our customer support managers are more and more similar to say a product manager trying to architect the ideal response process, response rules and user experience and this is just the beginning, know, we're investing heavily in this to make it much better, we'll include at some point support via phone calls, WhatsApp, So that's an obvious strength for LLMs because it's mostly about digesting a body of knowledge and exchanging communications with people and LLMs are have been trained on data that's quite similar to this and and already today are pretty good at it. Another area where we were we have seen the percentage of tasks taken by AI increased massively is data science, data analysis. Now our data scientists are doing much more here too, architectural work, building... Writing software and building tooling and processes for, say, the budgeting process or a recurring dashboard, but the actual fetching data, cleaning data, plotting data type of work is done. I don't have a precise figure, but I'm pretty confident at least 90% by AI. And then coding of course, we were at 0% written by AI two years ago, we're now... Last time I checked at 94%. Wow. Some companies, you know, depending on how aggressive you wanna be, you could be... If you're a good company, it could be at seventy and ninety nine, but no good company should be, I don't know, below 70 right now in my view. Yeah. There's no point.
00:37:46 · Anish Acharya
[00:37:46] S1: I agree. Everyone should be converging to a 100. Okay. I've got a couple more questions here, Luca, and then we'll wrap it out. So, you know, one question is, you've talked about how within Bending Spoons, every decision should be able to make with maximum rationality, but that doesn't necessarily mean always being led by data and analytics. And LLMs can also often create a sort of false precision. Is there a type of decision where you found being instincts led versus analytics led has led to better outcomes?
00:38:13 · Luca Ferrari, Anish Acharya
[00:38:13] S2: So I think analytics and instincts are inputs. So data is an input. My instinct that something is off or this could be cool is an input. You want to get as many inputs as possible within reason. But then I I don't think there's ever any case in life where you should be deciding with any other than anything other than logic and rationality. Again, it doesn't mean that simply because an analysis shows that, you know, people in France are more likely to buy this, then we do that and we do it in France. It's it's just again one input in a broader discussion, but I don't think there's ever a good reason to say we'll do this because I feel like it, you know, unless we're talking leisure, which is all about feeling feeling a certain way. Right. Even if you are the most... You wanna be as creative and imaginative as possible, but again, those are just inputs. Those were produced ideas or options that you wouldn't otherwise have considered, but you don't pick those simply because they... You got them in the spur of a moment and you're super excited about them. You wanna ponder them and determine whether that excitement is is justified, and if so, yeah, go for it. Absolutely. [00:39:20] S1: That makes sense. So maybe one question on behalf of founders who feel like they're stuck. You know, the definition of hell is never winning and never quitting. How should founders know when it's time to kind of move on versus... And of course, this is a difficult question versus, hey, maybe you're three months away from the next increment.
00:39:20 · Anish Acharya, Luca Ferrari
[00:39:20] S1: That makes sense. So maybe one question on behalf of founders who feel like they're stuck. You know, the definition of hell is never winning and never quitting. How should founders know when it's time to kind of move on versus... And of course, this is a difficult question versus, hey, maybe you're three months away from the next increment. [00:39:36] S2: Yeah. I got it. I wish I had a good answer. You know, I... I'm probably awful at that because my my my startup before Bending Spoons Evertail, we were worked at it for three years and you know how we failed. So it clearly wasn't working. We were trying to use AI way too early, clearly to write a diaries for people automatically. Clearly wasn't working, people were in news needs, certainly weren't paying for it. But we kept pushing, we raised a little extra money to keep going and pivoted and pivoted and pivoted and then at some point, we were left with maybe something like maybe 250,300 thousand dollars. But much of that was meant to be through a... A tax return from the Danish government at the time we were based in Denmark. I don't remember exactly the bureaucratic aspects of it, but something like that. Then we got a message from the government that refund either wasn't due ultimately or was due twelve months later, something that would basically that put us out of business and so they... You could argue that red tape made the decision for us. So I'm not very good at that. I feel I can't advise. Bureaucracy created Bending Spoons.