How to Thrive as a Professional with AI

This is an automatic transcript of the recording above. It is published in full and unedited, apart from correcting names the recogniser reliably mishears. It will contain mistakes.

00:01[music] >> Hi, I'm Tejas Kumar and I work on AI developer relations for LangFlow over at IBM. Today, I'm really excited to talk to you about AI development and how you can thrive as a developer in the age of AI. In fact, not just as a developer, but as a professional. In our time together, we're going to look at the shift that's happening between AI and more, let's say, traditional ways of working, specifically around coding, but it applies to a broad variety of disciplines. We're also going to look at this through the lens of science and research to ground ourselves in in reality as opposed to falling prey to sensationalism and hype. And so, I I'm really excited to take you on this journey with me, and towards the end, hopefully we can uncover clear ways that we can thrive, instead of just survive, in an age of AI. To get started, I'd love to just start by drawing a parallel um historically to the pre-computer days, back when there was a lot of paintings. Let's talk about the Renaissance. The Renaissance of human history was a period of artistic flourishing. I mean, we've we've all seen, hopefully, uh various Renaissance paintings. Um for example, um the one with with God reaching his hand out to Adam in the in the Basilica of of um the Vatican.

01:23Anyway, uh Renaissance art, um if you haven't seen it, Google it. It's it's got it's got this trademark feel to it, and that's because it was a time in history where everyone was enlightened and they wanted to paint, right? It was the romantic era. Um they didn't know, though, right? About about the innovation that was coming, the innovation of cameras. And And this is I I I draw a parallel here because that's kind of where we are today, right? Is um you have coding agents. You've got Claude Code, you've got Cursor. Um They do a lot of the job of an engineer. They just write code, and and sometimes it's even better than stuff I write, right? Um especially if you're learning a new discipline, like my background is is front-end or web engineering, and um you know, if if I wanted to get into Kubernetes, I would just have Claude Code do it and then explain what it's doing. I actually know Kubernetes. I have a Kubernetes cluster in production uh because of Claude Code, right? That that was better than me, and that taught me how to do this. And so, um we're facing a similar thing as painters to cameras, um where we're coders and there's coding agents. No doubt, if we rewind to the Renaissance, there would have been painters who, you know, saw the first cameras and and and must have thought, "Oh my goodness, this may replace me." Similar to us. Um but then, I I want to, on a positive note,

02:42also address some of those painters who saw the camera and thought, "Huh, that's a nifty new tool to do the same thing I do." Right? Because if you if you consider the painter and the camera, um both of them do essentially the same thing. They they capture light and represent that light on some type of medium, a canvas, a photograph, a paper. In fact, some can say painters do superior work to a camera because a camera just observes and captures photons, where painters can synthesize um photons. They can synthesis They can draw things that are are not capturable in reality, right? Similar to diffusion models like Flux by Black Forest Labs or Stable Diffusion, etc. So, um AI now has this capability also to synthesize art as opposed to just capture light.

03:36But if we consider portrait artists, right? They either paint the king or they take a photo of the queen. Um if we consider those skill sets, there was definitely a replacement. The point we're trying to make here is tools, and and this is maybe one of the, I'd say, leading ideas of this presentation, is that tools may vary, but there are also invariants. And by definition, invariants do not vary, they're constant. So, you've got variants that are the tools, and you've got invariants that are the constants. So, if we pull on this thread of painters and photographers, um the tool is the paintbrush, paint, canvas. Similarly, the tool is the camera, the flash, the lens. Um what is the invariant? And this is where we start to understand where we sit as developers in an AI world. In the photography example, or photography parable, if you will, there are invariants. The invariants usually come from first principles thinking, which is really when you strip a concept down to its invariant, okay?

04:43So, if we take a first principles approach to painting and photography, trying to identify the invariants, we'll see that the main invariant here is just capturing and retransmitting light. Right? And so, let's pull on that one level more. What is the invariant here? The invariant is or the invariants are light in the world. There's just there's light. There's light around us. Um we need some type of medium that encodes this light, um and that for us is air. It's literally the scattering of photons through air. Um in fact, the planet Earth appears blue because of atmospheric scattering, right? And so, we have this medium that encodes it, and we need a medium that decodes it. In in our case of light, um the human visual system, or the mammalian visual system, mammals, even the insect like all visual systems decode light in their own way. And so, there's three invariants here, light in the world, a medium that can encode it, and a medium that can decode it. These invariants are invariant, they're constant, and we can have a variety of tools to serve humanity along these invariants. The paintbrush, canvas, or cameras, lenses, or even diffusion models and AI models that can synthesize art. All But the point I'm trying to make is the tools vary, but there are invariants. And as long as we understand this, as long as we understand invariant versus tool, we're already starting to set ourselves up for

06:14success. Um everything else on top of the invariants are just implementation details with tradeoffs. An example of a tradeoff of a camera is you get photons exactly as they are. Um so, you can't be creative. Even with filters, you can't really be that creative. Um a tradeoff with paintbrushes is it's not as precise cuz you're not directly capturing photons, but the tradeoff is you can be as creative as you want, right? And And like Dali, for example, super creative uh artist. And so, the the invariants are constant, but the the modalities, the tools can vary. Okay, this is so important, that's why I'm spending a lot of time there. I mean, it's been like 7 minutes, but this is why we're we're doing this, okay? So, now what we need to figure out if we want to thrive in a world where maybe we're concerned something's going to take our job, is we need to hyperfixate on first principles invariants and adjust the tools. It's worth taking a little sidebar here and acknowledging AI will never be an invariant.

07:14AI will never be an invariant. Because if we think about invariants, they're typically laws of the universe. They're They're gravity is an invariant. Everything, even outside the Earth, experiences gravity, right? The Earth is held in place by the sun's gravity. So, mass and its gravity effects are invariant, they're laws. Um light is invariant. Um decoding light, encoding light, these mechanisms that have evolved over millennia are invariant. Now, if we put AI into this, say, chat room, is it invariant? Absolutely not. AI varies so much that there's a new tool breakthrough, etc., every week, right? Like I've heard a lot of people say they're fatigued, they struggle to keep you up keep up, excuse me. So, um how can we say AI is not invariant and will never be invariant. Okay, so then if it's not invariant by our current chain of reasoning, then it has to be what?

08:06A tool. Indeed, it is absolutely a tool. And since it's a tool, there's good news for us because we can just use it to solve invariant problems from invariant first principles thinking. So, then the question becomes, okay, if AI is a tool, how do we find invariants? I've already identified a few, but how do we like understand where an invariant is? And the best way to do this is to invoke what is called Noether's, or Noethers in German, theorem. Um Noether's theorem states that when you see a symmetry from a variety of angles, it means that there is an invariant there. Um and this may be too abstract, but let me let me put this into real If If you, for example, observe a spinning top, right? This thing's just like spinning on a tabletop. If you look at it from the left side, it's still spinning. If you look at it from the right side, it's still spinning. Um that symmetry is called rotational symmetry. No matter from what angle you look at a spinning top, it's still spinning on its axis. So, that is rotational symmetry, and according to Noether's theorem, um there's a law of the universe. There's an invariant there. Indeed, the invariant is the inertia and momentum that the top experiences. And similarly, um there's directional symmetry with gravity.

09:15Everything comes down, therefore there's a law, aka gravity. Um Noether's theorem is like, where you see symmetry, there there is an invariant. Okay, let's We've been We've been kind of in the clouds so far. Let's bring this down for us as developers. Um what are some invariants that we solve um as developers? And And if we zero in on the invariants that we solve as developers, we will thrive and we will be irreplaceable because we're solving against invariants, and we're using um tools to do that, fallible, variable tools to do that. Okay, so, um what are some invariants we can identify as developers according to Noether's theorem? Well, um one very strong invariant is agency. Is it like human beings, no matter from what angle you look at it, human beings tend to do better with agency and want a sense of agency. We want to be able to trust systems and and trust things and to trust people and we want to have agency over our time as well, right? Like I I want to know like the times where I've been the most depressed is anecdotal was when I feel like I am just spending all my time with work and then somebody somebody else needs my time and this and everything's on fire and low agency leads to negative health outcomes. Similar for identity.

10:37Agency and identity are invariant with human beings. In fact, 2021 paper out of Harvard University and the University of British Columbia titled the positive influence of sense of control on physical, behavioral, and psychosocial health in older adults and outcome wide approach. This paper from Hong and colleagues was was tremendous to prove this. It was an 8-year long study by Harvard's human flourishing lab and it looked at 12,998 people. Just imagine 12,998 people. All of these people were adults over the age of 50 and usually they chose that because adults over the age of 50 typically tend to have less agency than than others, right? They like when you're young you feel like oh I can do this, I can go here, I can do this. When you're when you're older and you're retired you tend to feel like you maybe you're you're past your prime and so on.

11:28So they chose this and it's a massive cohort and they took 8 years. And what they did was in the first as soon as they started they took assay of these people's sense of agency. Hey, how in control do you feel? How much agency do you feel you have over your time, over your identity and so on. Four years later they took another survey. Same thing. How how's your agency and so on. And then 4 years after that, so in the 8th year, they identified these people's physical and mental health capabilities. And what they found was people who reported relative to control so within 4 years if they reported I feel more agency these people also lived longer, had better psychosocial health and better physical health as well. Like every every marker went up. And so we can observe this I mean 12,998 people over 8 years. Like that's many many angles. And if we observe this over many angles we can see an invariant no doubt is human agencies. I want to feel in control.

12:25Pause and consider yourself. Like the reason you get frustrated when you have to click on cookie banners, right? Is because you value your time. You you want to do you want to get back as much time as you can so then you have the agency to do whatever you want. Spend it with family, go out with friends, whatever it may be, right? And so for us as human beings we agency is a strong invariant. We want to reduce uncertainty and risk so that we can preserve our resources. I don't want to lose a bunch of money accidentally and then I can use my agency with the resources I have to do what I want, to be happy. We want to get back more time again because time is very closely correlated with agency. If I have a week free I can do whatever I want literally. I I want a week to do whatever I want. That language is agency. There was a there was a study by by Google in 2009 led by Brutlag that shows that when they so they did an experiment they doubled the latency on their search this. So from 200 milliseconds they raised it to 400 milliseconds just to see what would happen and 0.6% of people just stopped using search because it was too slow, right? Because it impinged on the agency and said hey you don't you don't actually you you lose time here and if you lose time you don't have as much time to do as whatever you want etc. etc. So agency is is the invariant we protect.

13:48And and it usually manifests through risk and uncertainty or time or resources like money and so on. Let's let's make it really practical and think about this from a software engineering perspective. This means we provide as less friction as possible in terms of UX and allow users to do the most meaningful trustable work in the lowest amount of time. That's the whole invariant we solve as software engineers. So let's consider a calendar app. Let's make it super practical. We're building a calendar application and and there's a demo coming up here. We build a calendar application. Um and people don't use a calendar application just to browse and book. Like it's not that simple. It's not like I just want to look at my calendar and book something. It's more I people we all use it to find the best time and then to protect me in case somebody cancels or if plans change. Like it's the need a calendar app solves is not just browse and book. It's find the ideal time and protect me in case of uncertainty.

14:54If we consider this example even for like travel booking, right? It's the same thing. It's not just like I'm going to go find a flight and pay for it. It's more I need to find the best flight at the right price point and I need to be covered in case the flight is cancelled. Like we always want the most optimal thing that preserves our agency and we need to continually preserve our agency in case of uncertainty meaning I need some type of insurance. I need to know that this is the best one, the best time slot and in case someone cancels I'm covered. I get a notification they don't book an event in parallel to mine etc. So those are the needs, those are the invariants we solve. If we identify those invariants we can do so with software. We can solve them with software either by writing code like Google Calendar. Google Calendar is a great tool. Or this is where we can even use AI agency for ourselves to thrive as developers. And so let's explore both of those now just like by way of a demo here. So I've got my Let me let me open up my calendar.

15:54This my calendar. Welcome. And as you can see I have quite a bit of agency. And so you know I can I could find okay what's the best slot for lunch with Polly? It's probably here. So I'm just going to do like lunch lunch with Polly right here. Okay. So now I did that and I found the best slot and I know like if it's uncertain. So that's I've met my need, right? I've I've done it. Um That's that's one tool. We just use software. Or we can use AI which again is not invariant. So I can do the same thing with AI. So let's let's go back here and this time I'm going to use LangFlow. A great way I'm going to build I'm going to literally just build my own AI agent, my own personal AI agent for me cuz I can with LangFlow. LangFlow is open source. It runs locally.

16:43You can host it if you want. It's really you do you, you know? But it it also helps us reason about first principles agents. That's why I'm going to use it here today. So let's go and add a chat input. I'm going to zoom out to 100% and then we'll do a chat output as well because I want to be able to talk to my agent of course and then we'll do an agent right here. That's my agent. Hi agent. And I'm just going to wrap up input and output just like that. And I'll go to the playground. I'll say hi agent. Do you work? Yeah cool. Seems to be working. Now I'm going to give this agent access to my calendar. Again we're solving against the same invariant but in different ways, okay? I'm going to give this access to my calendar. So I'll come here and I'll get Google Calendar right here.

17:35Um and this is using a great tool called Composio. So now what what can I what do I want this agent to do for me? And notice I'm using the word agent here on purpose because the purpose of AI this is the big thesis, right? This is the point. Don't miss it. The purpose of any tool is to serve an invariant. The invariant with human beings and software is usually agency and that's why we have AI agents. We have agents to do the stuff for us to preserve our agency thus serving the invariant. That's the point of this whole talk. I hope you got it. Okay, so let's let's preserve my agency. So I want you to be able to update calendar list entries and we'll turn on tool mode and we will Yeah, now we can do many things. That's what I wanted. So we can let's let's select none. Let's see you can update a list entry, you can update a calendar, you can create an event. Actually I want to scope this a little bit. Um you can list events, you can move events.

18:34You can find events. You can um quick add events. I think this should be fine. Can you insert events? Yeah cool. This So these are the things you can do. These are the things my agent can do. Check it out. Just like that. Let's use 4.0 cuz it's a bit more capable. And now you know I can go here. Okay. Sure. March September 2025, right? Um and let's And so it's going to find an event. There your lunch with Polly is scheduled for today September 9th, 2025 from 1 to 2 p.m. European Cool. Let's say now let's go back to the calendar. It's it is 1 to 2 p.m. Let's say move it to from like 12 to 1, right? So we'll say move it an hour earlier. In fact, let's just open this in split view. Right? Um and the agent that I'm building for myself should just do job.

19:29So let's watch here maybe. There we go. So it created a duplicate and made it slightly longer. Let's say um delete the one at 1 to 2 p.m. And I I I want to focus on this on purpose because there's some really great points here. Um See this? Do you see this? Delete the one starting at 1:00 p.m. And now um come on. It's going to find the event. And it seems it's already been deleted. I think it I just didn't give it permission. So, I I all of that, by the way, was super intentional. I wanted you to see that um because what what let's go what what is the invariant? The invariant is my agency and my time. Um did this exercise, this demo, serve my time or my agency? It yes and no, right? That's kind of where AI is today. It it it did in that cool, I could make a calendar event by talking or or by typing. Um no, because the AI messed I made a double one and then I didn't know how to delete it. And so then I ended up losing time and losing agency. This is exactly where we find ourselves with AI today. Um all of this what was on purpose, right? is we get out of AI service towards the invariant as good as our input and our tools. This is also where we find the space for something called context engineering. Unfortunately, we don't have the time to go into that. If this was like an hour presentation, we'd spend a deep dive on context engineering here. But with AI as a tool, we need to know that garbage in equals garbage out.

21:11If you can provide really great context, for example, I could have provided the right context in my prompt and said do not create a new event, move the existing event, etc. etc. I could have done that. But then there's also a time cost there that eats into my invariant, my agency. So, I want to do more with less. And in this case, if I think about context engineering, it's doing more. Are you understanding the nuance here? I hope you are cuz that's the whole point of this talk. Um ultimately all software must serve the invariant of human agency. Um and and we it does that by faithfully representing state. This is your calendar. Faithfully and reliably transforming state. I'll make an event, I'll move an event.

21:58And faithfully preserving the main invariant in between state representation and state transformation. I hope that's clear. That is like the entire purpose. The tools do not matter. AI can do it. It will be able to do it way better over time, right? People are saying AI is in a state of exponential growth. So, what you just saw will probably not happen a week from now. Um but where it is right now is it is a tool and a means to serve the invariant. Right now, you might decide, you know, cookie banners, Google Calendar is better. Cool. That's fine because ultimately we're focused on the invariant. Um as software serves the invariant you also need to have a deep your product, whatever it is you're building as a developer, needs to have deep knowledge of the domain specific invariants as well. Um some things are just non-negotiable. For example, you're working in a bank um and what must be invariant is user privacy, right? Like you must not you must store probably all your secure tokens on some type of secure hardware enclave um so that nobody can read or write like Face ID data. That's a domain specific invariant that you must also preserve and protect.

23:08Um another invariant is that good software is typically item potent because clocks drift, packets are lost, networks partition. Um and in the case of a message arriving twice, if it's the same message, you need to maybe do the operation once. You need to have item potent and durable systems that can recover from errors. Another invariant, as we talk about laws of the universe, is that things go wrong. Network connections drop. Packets get lost, clocks drift. And so in light of those invariants, how can we serve our users? Um finally, there's something to be said for authentication. What I just ran this example was local on my device. Um nothing left my device except an integration with Google Calendar.

23:51But the entire AI agent, LangFlow, whatever is running on on my device. Open AI just generated language, right? Um you may obviously need more security. Instead of talking to Open AI, you may need to use something like Ollama or vLLM. LangFlow has support for Ollama. Um but that's another invariant is identity. How much identity are you willing to share, etc. Again, this is going to be different for health care versus, you know, consumer apps and so on. So, um that's something to think of. I'd like to start wrapping up by giving you a checklist for builders across a variety of form factors um really to serve the main question of how are we serving our invariant with software, which is personal agency. Um and and this is what you need to identify.

24:33Anytime you want to solve a problem keeping in mind that AI or Cursor is just a tool um you need to have answers for these. Question number one, what are our domain level invariants? I just gave the example of banks and hospitals. What in your specific domain are invariants that you need to engineer against? Number two, um where do we lose time? Uh with authentication, with maybe language model generating a lot of text. Where do we lose time and how can we buy it back? Because that again serves agency. The more time you save your users, more agency they have, the better you build software. Number three, um what in my application needs to be trustworthy and proven to be trustworthy? Um something like authentication with passkeys, you need to prove or something like end-to-end encryption for journaling application, right? This needs to be provably trustworthy. You need to say anyone can verify the integrity of this, that nobody can read notes in transit, um end-to-end encrypted notes, okay?

25:32So, those are the three questions. What are my domain level invariants? Where do I lose time and how do I buy it back? Two That's two questions in one. And third is what do I need to be trustworthy and provable um that it's trustworthy, okay? Um let's wrap up by talking about AI as a tool. AI, no doubt, is great. Um but again, we're talking about AI in the context of a tool and as invariants. Um many of you here are using Cursor. Um and let's think about this in in the context of invariants and first principles. We use Cursor because the promise of Cursor is it preserves our agency by giving us time back. All right, I don't have to think a lot. I just like vibe code. I just say, "Hey, make this thing." and it makes it. Um the the problem is um while it does give you a 10x speed boost, you can say, um it also gives you a 10x speed boost to ship the wrong thing.

26:25Right? Uh and so we lose time curating Cursor's output which may be the same amount of time that we spend hand coding things instead of vibe coding things. You know what I mean? And so I would love to see, maybe I'll perform it myself, a randomized controlled trial just a completely clean research experiment um done with a vibe coding group and a non-vibe coding group um just to see like who actually gets it done faster. Because my thesis right now for complex problems is that Cursor gets enough things wrong and you have to manage the agent um that eats similar amounts of agency or time as hand coding things. Similar to this calendar example I shared here. And so, um are we going to be replaced by AI?

27:09How well do we serve the invariant? That that should be the question, okay? Um similar many of you are using ChatGPT. ChatGPT serves the invariant of agency by giving you control. For example, you ask a question about something you don't know. Explain to me the citric acid cycle, right? And ChatGPT will give you some authoritative answer and you feel like your agency is served cuz you have more of a sense of control. Um the danger here is it can hallucinate and be confidently wrong. In fact, it was. In preparing for this, it was many many times confidently wrong. I I did use it. Um and so then you've got to go check the sources, you've got to verify. You still lose time and control cuz you're just doing the work anyway.

27:51So, what is the net agency win there? Right? Um and this I I will I'm I'm wrapping up, I promise. The main question to ask the main question to ask when building software and when we want to thrive as professionals is what is the net cost to my invariant? That's it. And if a tool has a lower net cost or a higher net benefit, it wins. Right now, AI does not have a lower net cost, at least in the examples I shared. Um don't even get me started on trust, right? Like a lot of people ChatGPT has a great um agent feature where it can literally like book tickets for you. People don't use it because it's I mean, how do you trust that with your credit card information? So, there needs to be more engineering done towards trust again in service of the invariant, aka personal agency. In fact, many of you listening to this, I hope you feel entrepreneurial enough to build that yourself. You can. You can just build things and I I encourage it, okay?

28:50Um last thing before we wrap up. I I promise. Is I wanted to explore the question. Maybe we'll do another talk about this. Um taste. A lot of people say taste is is the thing that separates humans from the machines, taste, you know? Um is taste invariant? Meaning, if you look at taste from a wide variety of angles um according to Noether's theorem um is it constant and is there some type of law around taste? I don't know. Uh but I do know that Apple, for example, has been known for its taste. We all love iPhones because of the taste that goes into it. We love a specific style of UI and UX because we can tell it's tasteful. We like specific dishes and food and snacks because they are to our taste. And it it's an interesting question because is taste just the aggregation of the majority or is it something more? That's a question for further exploration. I have honestly nothing to share here. I just thought it was an interesting one. Um if you have any thoughts, please come up to me and talk. I'm I'm here. Um or um if you wanted to leave a comment, that that'd be great as well. Um let's let's wrap up and then and then we'll end this. Um the main the main wrap up is this. Um How do we thrive as professionals?

30:15The main answer is to identify invariants and then solve them from a first principles approach. Um the the thesis of this talk is that the chief invariant of software engineering is personal agency. Um and there are multiple tools to solve those problems. AI is one, um but there are others. If Whoever can solve against an invariant for the lowest possible cost wins. Thank you very much. >> [music] [music]

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