Can Your AI Strategy Be Future-Proof? | Galileo’s Vikram Chatterji

0:07Welcome to Dev Interrupted. I'm your host, Ben Lloyd Pearson. And I'm your host, Andrew Ziegler. In today's news, we're talking about how banks are capturing tech talent, why your AI can't see this gorilla, and how the skill of the future is focused. Wait a second, did you just say that AI can't see gorillas? Yeah, well, you ever hear that saying about not seeing the forest for the trees? Yeah. Well, it turns out if you're an AI, you might not even see this literal gorilla drawn in a scatter plot. My favorite article from this week was one called Your AI Can't See Gorillas. And it explores a study that demonstrated how when you're given a hypothesis to prove or disprove from data up front, it challenges the way that you can explore that data freely.

0:55And basically, there's a literal gorilla depicted in a group of data that when depicted on a scatterplot, it shows that gorilla. But if you don't actually plot the data on a graph and look at it and explore it, you're never going to see that gorilla. If you just go in and look at the columns and the rows and what's in the cells, you're never going to see it. For me, it really highlighted the importance of intuition and curiosity and how we as humans explore data. Because AIs, they totally fail at this right now. Even in the article when the author would take a screenshot of the scatterplot that the LLM is so helpfully rendering for it that shows the gorilla plane as day.

1:38It still doesn't want to see the gorilla because it thinks it's looking at a chart. So for me, this is spelling strawberry all over again. And it really emphasizes why it's important to keep a human in the loop too. Yeah. So I guess the generative AI comes from a universe where scatter plots are never used to visualize things or to make pictures, you know, but so, all right. So if I understand this, if you plot the data on a graph visually, the human is almost certainly going to immediately understand that it's a gorilla right now. Literally waving. We'll make sure we share the link so our readers, our listeners can see it too.

2:14It's quite hilarious. Literally waving, but it can't see it, no matter what you ask it. Yeah, and even humans, I mean, if a human just looked at the data and never visualized it, they would completely miss the plot here as well. So in other words, get off the command line and look at some images for a moment, you know? Yeah, and sometimes it's even applying common sense. And I think the biggest takeaway here too is it really highlights the difference in which you and the LLM are processing data. This is strawberry all over again, literally, because we don't struggle with spelling strawberry or knowing how many R's are in it because we look at each individual letter.

2:51But as we all know, LLMs don't work that way. Similarly, in this situation, we learn from data and graphs by looking at them with our eyes. but an LLM understands them by looking at the actual data. Yeah, and for our listeners who aren't aware of the strawberry phenomenon, there was a while where ChadGPT would gaslight you to convince you that there were, what was it, two Rs in strawberry? Oh, it would commonly change. It would really never get it right. Yeah, it'd never give you the correct answer no matter how you ask the question. And I believe they had to manually fix that one use case to get it to work.

3:27Yeah, the strawberry news was tragic. and very viral. Perception is a hell of a drug, especially when you combine it with pattern matching like us humans commonly do. So were any of the GPT models they tested actually able to detect the existence of this gorilla? Not successfully, no. Even when given the picture. However, it was noted that Claude was able to kind of start to pick out that there's some sort of depiction in the image. It doesn't go as far as to understand that there's a gorilla, you know waving at it in the plot but it does start to understand that there might be visually something depicted it goes one step further they even say that because there's an image depicted in the data it's highly likely that that data is generated or not authentic in some way and so then claude warns the reader about the data they're using and its authenticity which is a fun twist for the story to end on yeah it's almost like it detect it detects that like something is waving at it, but it doesn't think that it's important to know what that thing is.

4:30More importantly, it knows that in its world that scatterplots don't wave at you and scatterplots don't have pictures in them. So the fact that this one does both something suspicious. Yeah. Now I want to switch gears to this, this story that you brought up about banks, you know, in financial institutions trying to catch up and the battle for getting, for hiring developer talent. So I read this article and, you know, I really broke down how, financial services industry as a whole is trying to seize an opportunity to hire more top technical talent from all these big companies that are out there undergoing layoffs.

5:05And I think these layoffs have become such a seasonal occurrence that many of these big tech companies are kind of losing their allure. It's like a certain number of people are going to get laid off every January. That's not a great environment to work in. And traditionally, I think a lot of developers, particularly from these big tech companies, they gravitate towards those companies because they just have like, they want to work there. Like they pay really high salaries, they're great institutions, and they've not really viewed financial institutions in the same light. But the reality is a lot of these organizations are now modernizing their tech stacks, they're increasing budgets into development, giving them access to all the latest and greatest tools like AI and machine learning and all the cloud technologies that we know and love.

5:53And compensation is also going up with this, as well as flexible work arrangements and the benefits that they offer to be more in line with the big tech companies. And there's actually been – there's an element of geography too. U.S. banking hubs are a lot more dispersed. So I think as a result, it's easier for them to get into the remote work movement and to find developers that maybe would work remotely for these big tech companies but live in a city where these banks have a headquarters. But the main reason I brought this up, because it reminds me a lot, actually, of a conversation I had with Louis Vega from Bloomberg LP back in May of last year.

6:32And he builds all these really cool internal tools for Bloomberg. I really loved learning about the engineering culture they built because, you know, one of the coolest aspects was how he was describing how they create these like unique brands for each of their projects. So like a log manager that you call logger, like without an E, and then you go to a designer and get a logo for it, gets added to this internal marketplace for all the other developers of the company. Like they really kind of celebrate building cool things and sharing them internally. So if you haven't listened to that episode, I definitely encourage you to check it out after you read this article or before you read it.

7:09Yeah, definitely a good one to check out. I also really resonate with what you're saying about what you learned from this article. I do think it says a lot about how tech is being more distributed everywhere and technology is not something relegated to the tech world per se. The entire world's undergoing transformations with technology and AI is pulling that even faster forward now. So the result is there's so many opportunities to even be a tech person within a non-tech company. That's how I actually started in technology. By being that technical expert for non-technical folks that are trying to execute something at scale, you can actually find that your insights can go really, really far in places that are maybe non-traditional to where you work from.

7:56And this also reminds me of even some of the insights that I learned from today's guest, Vikram Chatterjee of Galileo, who's coming up here in just a few moments. And in order to implement AI at the scale you're talking about in a bank, you have to prototype rapidly, but you have to measure that impact and ultimately build trust in your organization, which is probably going to be very rigid against that kind of rapid change. But before you can get there, you have to attract that top talent and catch up people-wise. So this news article makes perfect sense to me. It even relates to what we saw recently with Goldman Sachs acquiring that AI transformation head from a tech company.

8:37I think this is a trend we're going to see over and over again this year. Yeah. And one thing I've actually seen some specific examples of is when a lot of these more traditional organizations launch a more coordinated software development arm of their company, they're often set up as almost like an independent entity that gets a lot of freedom that you don't typically see within a financial sector company. So that allows them to move a lot more quickly to use new technologies. So yeah, you can't, you know, banks don't write them off. If you're one of the unfortunate people out there who's been laid off, or even if you're not like, you know, go check them out.

9:12Maybe they're a cool place to work. But, you know, speaking of your skills and where your skills can take you, there's another article I read this week that talks about how the skill of the future is not AI as everyone's talking about, but it's focus. And this one, I loved this article. it really highlighted how leaders need to create a culture where engineers can validate and understand AI's inputs and outputs and seek alternate solutions and understand ultimately what's happening underneath. That way you can engage in a stronger understanding of how the AI is working, but also then harness that understanding to get really good uninterrupted work done.

9:51In sum, basically use AI and understand it, but then harness that free time and focus on the stuff the AI can't do. And it really talks about how you build a team culture, the harness that unlock. Awesome. Yeah. And it kind of reiterates a point that I've been making for a while is that, you know, a lot of these GPT models are, they're very accurate, but they lack precision. So what I mean by that, and a good example of this is if you go out and ask any of these GPT models to create a picture of a waving gorilla, for example, they're probably we going to create something that, you know, if I took the output and handed it to our producer, Adam, and asked him what it is, he would probably immediately say it's a waving gorilla.

10:34Like they do that quite successfully. But if you look closer, you'll probably see a lot of like micro errors that it makes, like, you know, to borrow a phrase from Westworld, the answer's in the hands. Like you can tell that it's fake or it's AI generated because the hands look weird. And in that environment, it's actually very difficult for humans to find errors, right? If everything on the surface looks correct, but it's actually filled with a bunch of problems, you need focus to suss out those problems. And it goes to show like we've been seeing research and anecdotes of the difference in how junior developers and senior developers adopt AI.

11:09One thing that's come up again and again is that junior developers are a lot more likely to just blindly accept AI output and then try to fix any problems after the fact. Whereas a senior dev might treat it more like a sparring partner where they ask it some questions, get some ideas from it, and then the developer themselves goes out and creates the code themselves, you know, asking for advice where they need it. In that environment, you don't have to catch as many of those precision problems. Well, some great stories we had today. Andrew, why don't you let us know about who we're having on the show today?

11:42Yes, we've talked about how AI is changing the way engineers work, but also the risks of blind dependence. And if we treat AI as an oracle instead of a tool, then we lose the ability to question and refine and truly understand the solutions it generates. And focus is the real skill that separates great engineers from the rest. So in today's conversation, we're talking with Vikram Chatterjee, the co-founder and CEO of Galileo, about how to future-proof your AI engineering investments. Galileo is building the system of record for AI models that help teams open the hood and understand what's happening with their LLMs underneath.

12:23Stick around. Habits are a powerful thing. Maybe you've read one of the many books about the habits of highly successful people out there. Well, Linear B is out with their own book, The Eight Habits of highly productive engineering teams. This practical guide offers advice and templates to help you establish durable data-driven habits. It covers things like setting actionable team goals, coaching developers to level up their skills, using monthly metrics check-ins to unblock friction, and run more efficient and effective sprint retrospectives. This guide has something for everyone within your engineering team, so check out the link in the show notes for the eight habits of highly productive engineering teams.

13:08Hey, everyone. Welcome back to Dev Interrupted. I'm your host, Andrew Ziegler, developer advocate at Linear B, and joining me today is Vikram Chatterjee, co-founder and CEO of Galileo. Vikram has been on the front lines of the AI revolution for many years, from leading product management at Google during the birth of Transformers to building tools that help engineering teams confidently evaluate and deploy AI systems. Here's the crux of today's conversation. Engineering leaders are stuck between a rock and a hard place. They know they need to experiment with AI to stay competitive, but they're under immense pressure to justify those costs.

13:50All the while, AI evolves so rapidly that today's wrong move could cost them tomorrow's opportunity. Vikram, welcome to the show. Thank you, Andrew. Super excited to be here. Likewise, let's jump right in. Starting with the biggest challenge currently facing engineering leaders, AI being that moving target and experimenting feeling risky, it's a big barrier to adoption and the fear of making the wrong investment is ever present. How do you think leaders can take the first steps without putting themselves or their teams in a bad position? I've always thought about AI as it's another tool in your arsenal, right?

14:30even before generative AI became a very big thing with classical machine learning and with NLP, it was never about like, hey, you have to use this thing. It was more about what's your use case. And based on that, is this a good fit for your use case? Now, I guess the difference is what we've seen with a lot of engineering leaders that we talk to, there's a lot of top-down pressure to just use AI for the sake of it. To get to your point about the rock, between a rock and a hard place. It's very important for engineering leaders to think about what are the heuristics, right, that is going to help me figure out, you know, is this something that we should be even going ahead with?

15:08And that includes things like, what does the business need? Because what I've seen is folks just do a massive hackathon within their org, and you're going to get a hundred ideas just given how open-ended AI can be right now, right, in terms of whether it's generation of text, whether it's generation of images or completion of a task with agents, you get a hundred use cases. But it's very, very important for them to then go back to like, you know, product and business owners to figure out which one of them should they prioritize. And also on the back of that, which one of them can you actually get out the door very quickly?

15:44And that's kind of where the operational rigor has to kick in of like trying out X number of ideas very, very fast. So you have to have that machinery in place and the ability to push back and say that, hey, maybe I don't need to use AI at all. But when I do, here's how I need to do it. And at that point, we can talk about this more, but we have to think it through in terms of if I succeed, what does that mean for me in terms of the number of engineers I need for this, the evals, the cost of productionizing this thing at scale. So there's a bunch of things that people have to think about and a lot of trade-offs at the onset itself.

16:16So it sounds like in balance with that proliferation of ideas, you really need an evaluation framework or a way to understand and extract scenarios that are maybe have a higher ROI or a higher impact on your business and focus on those. Because I think that's kind of part of the problem, too, is you're drowning in possible solutions and everyone can come up with maybe a way to integrate it in some way. But is that the most effective way? Is that where we should focus our attention? and the more attention you put on something, it can skew than how the rest of your organization is using and thinking about AI.

16:50So those decisions, especially early on, they're really impactful. How would you advise or what habits do you think make for somebody to be able to evaluate and de-risk that experimentation? Like what are those tools that those kinds of people are always using again and again to do that well? Yeah, it's a great question. I will say it depends a lot on the organization and you have to just know your organization well. So if you're a big bank, right, the amount of harm that can happen if you put something out there in the consumer world with AI and it misfires is very, very large. It can literally derail your entire bank's reputation.

17:30For a commoditized entity like a bank, you're going to be out of business very quickly. On the other hand, if you're, let's say, a DoorDash or an Instacart, the bar is probably a bit lower because not to say that they have a low bar in general, but if something goes wrong with their chatbot or something like that, it's not going to be the end of the world because they're not dealing with people's money. They're dealing with hungry people, which is bad, but they're not dealing with people's money. Maybe you didn't get my burger, but, you know, my bank account didn't have like an unauthorized transaction or something.

17:59They're totally different stakes. They're totally different stakes. And so what I've seen as a result of that is when you talk to these large enterprises, they're very, very excited about generative AI and they're very excited to add agents and everything else. But they're taking a very, very careful crawl, walk, run approach to it, which I think is good. What I'm seeing with the other companies where they are earlier stages, they're tech first. Let's say, as an example, like a DoorDash or like an Instacart or like an Airbnb or a Twilio, they're taking a much more experimental approach to this.

18:29right like let's try things out let's see how it goes let's see how we feel it's very much on the lines of you know build fast break things learn quickly and that's really led to them kind of figuring things out as they go and it is also useful when as this industry is moving super quickly so based on the organization i've seen like the barrier to entry from a fault tolerance perspective is different now within that if you're an engineering leader at a faster moving company if you're all about experimentation and going fast then the question becomes how do you plan well and there is a certain crawl walk around there as well to be honest andrew because what we've seen there is you have to think about you know is it going to be 15 use cases 10 use cases you have to have some forecasting there because once based on the forecasting you have to do a couple of things you first think about the compute costs and then staff yourself with enough i don't know a100s and have that like available for you so that everyone can just build otherwise everyone is going to come back to you and say like, hey, where's my GPU at?

19:29So you have to have like X amount of compute and give that out very judiciously. You then have to think about like, how can you optimize the compute and, you know, start to invest in tooling at that layer to minimize the cost of compute. And then comes the eval piece, which is kind of what Galileo does, where you have to think about how do you create the right kind of guardrails in place for like an AI, CICD process, and then basically go to the teams and say, awesome, all right, you want to you want to launch things, you want to experiment with things. Here's the stack that you can use. Go knock yourself out, right?

20:01And some people will use the entire stack, some won't, but you have to create that enablement almost within the team before they can like go crazy. That's a big unlock. So let me try to unpack this playbook because I think there's a lot of really interesting tips in here. One of them being first and foremost, understanding your company, your company culture, their risk tolerance and what they're doing, and understanding that there's a big difference between a traditional enterprise and a digital native company experimenting with AI right now, especially with different levels of risk within what they're working on.

20:35So it's about understanding your own company, your own environment, the level of risk and the tolerance for experimentation. But then getting into that, you know, crawl, walk, run loop that's going to get you up and get you moving on this process. It's about creating the actual resources to enable those teams to make effective tools. And so if you're going to create a way for them to grow within your company, you have to think about resourcing them and prioritizing them based upon, again, you know, maybe the profile of your company culture. So it really does start with understanding your culture.

21:11Exactly. And you hit on a good point. It's the culture, it's the people. Again, it comes down to like also the business that you're running, whether you've an Instacart or DoorDash, again, going back to that example, they have many different ways of instituting general AI, but maybe there's a different company that really doesn't have those many use cases and that's okay. You kind of have to look at all of those different angles and then figure out how you want to act and how quickly you want to act. But it does stem with that. As an engineering leader, I would say like step one is always just that.

21:42Right. And part of company culture and it's kind of what I want to focus on next is also, you know fears and anxieties around making the right or wrong decisions or creating tools that are doing jobs that traditionally people did within their org and understanding how people need to reprioritize their time to better use these tools and in doing all of this i think we're all trying to make systems that are future-proof we don't want to rebuild these ai workflows again and again and again we want to make it once and evaluate it and iterate on it So that's part of justifying to the ROI and going back to creating the resources within your team for that to grow.

22:22So how can an engineering leader, perhaps someone who's in a company culture that's maybe more in the digital native side, have an appetite for risk and experimentation? Maybe they even have a little bit of resources. How can they shift those conversations with non-technical leadership from immediate gains to creating future-proof solutions that are going to help the company in maybe like a year or five years from now? It comes down to building trust. I think the first thing becomes like as an engineering leader, I've talked to a lot of different leaders in the space and it all comes down to number one, first having a gut instinct reaction, a gut instinct on their own side, personally.

23:03Like some kind of a trust that You know, these use cases are great. And this use case is actually going to be a good one to start with. The second is staff that. Just go very, very fast and build out a prototype and start to see, does that actually add value? And then shop that around with others in your business. They could be leaders in the business, depending on how large the company is. And you can now with Gen.AI, the unlock is that you can build a prototype pretty quickly. And so you can at least start to get a sense for like, what is the appetite here? And that's typically what I've seen happen.

23:34and then from there you can start to figure out what the KPIs are, right? Because the main thing is you want to be able to ship something pretty quickly. You want to go to production quickly with the right checks and balances in place. So you start with at least one to two use cases as quickly as you possibly can with the right checks and balances and guardrails in place and then start to see how that looks. Roll it out to 1%, 5%, 10%. Start to see what you learn and then with that playbook you can go very fast with everything else. This is exactly what we've seen with the largest banks in the world, as well as the digital natives.

24:09It's just that the digital natives are just moving much, much faster than the largest banks. But this playbook is kind of similar on both sides. It makes a lot of sense in terms of how those teams can get started. It's also about evaluating, going back to the evaluative frameworks from earlier and having measurements in place to look at the results week after week. And that creates within the company culture, a healthy socialization and understanding of the tools that people are building and we're using. And that's what cultivates the trust. And it's also something really hard to find in an LLM based world.

24:44You know, LLMs are stochastic. They don't want to repeat themselves. So when you put them into an environment where you want a repeatable workflow, where you have an AI agent evaluating things on the fly, there's so much to consider. And that's kind of part of, you know, the dauntingness of that I was alluding to earlier. In our initial conversation, you know, there was something that you mentioned to me that's really resonated with me since. And as you mentioned how AI agents in the future, or even now, they can act as smart routers or kind of like load balancers for workflows and help optimize them over time.

25:19And that was really fascinating to me. I'm wondering if maybe you could dive into that a little more just on a technical level now that we've talked about introducing these products and or these projects within your company. if you are somebody who's revolutionizing a workflow right now with AI, how should they best kind of think about it? What I'll say is in terms of agents in particular, how that works. So for context for folks, Galileo is a leading provider of evaluation tooling for any AI developer out there that's building an AI app, right? And what that means is an eval basically includes not just the metrics, but also like the data set that you're working with.