AI, ML, and Data Engineering InfoQ Trends Report 2026 Transcript, AI Summary & Key Points

00:01 The decisions you're making right now about AI adoption, architecture trade-offs, and how your team works together will shape your systems for years. Getting those calls right when the landscape is shifting this fast is hard. Cuc Francisco has spent 20 years connecting senior engineers with practitioners who are a few steps ahead on the same problems.

00:16 This November 16th through the 20th, 60 plus speakers across 12 tracks will share what's actually working in production and what isn't. No hidden product pitches, just senior practitioners helping senior practitioners. Learn more at ucconsf.com. Hello everyone, welcome to the 2026 AI and ML trends report podcast. Greetings from the InfoQ AI, ML and data engineering team.

00:46 We also have a special guest today for this year's trend report discussion. This podcast is part of our annual report to share with our listeners what's happening in the areas of AI and ML technologies. I am Shini Panchikala. I serve as the lead editor for the AI, ML and data engineering community on InfoQ. I will be facilitating our conversation today.

01:07 We have an excellent panel with the subject matter experts and practitioners from different specializations in the A and ML space. I will go around our virtual room and ask the panelists to introduce themselves. We will start with our special guest first. Chip Huen. Hi Chip, thank you for joining us and uh participating in this podcast. Could you introduce yourself and uh tell our listeners you know what you have been working on.

01:34 >> Hello, I'm Chip. I'm very sad to be here today. Uh it's an honor. I know that everyone on the panels has amazing background. I've been following your work for a long time. Today I was hoping to share what I know but also hoping to learn from you all. I'm working on a lot of AI stuff AI agent tech getting AI to work well for like workflow. I'm so very interested like making AI just like faster and cheaper.

01:55 We saw recently the types of AI that came out. I think there's a huge demand for cheap and fast intelligence and that's very exciting. >> Thank you Chip. Yeah, definitely if we have time I would love to learn more about this Java model from type safe AI if you have any experience on that. Daniel, how about you? Hi, I'm Daniel Dominguez. I'm the deputy editor at InfoQ.

02:14 I focus on artificial intelligence, machine learning, and data engineering. I'm also software engineer, and lately I've been particularly interested in AI agents, agentic software development, and how AI is moving from purely digital system into robotics and physical. >> Thanks, Daniel. Arthur. >> Hi everyone, I'm Arthur. I'm a researcher and technology strategist basically and these days I split my time between consulting advising companies about how to make AI useful in your systems and apply research basically on

02:48 multi- aent systems and belief vision. >> Thank you and hen. >> Hi everyone, my name is Hinglu. I mainly focus on ML infrastructures as well as I guess nowaday engineering. Currently I'm helping with a startup on consulting on AI inference and a longterm fan of QCON also an editor of info Q also a QCON program chair for QCON New York. >> Yeah thanks definitely I very excited to talk with you all about this topic because uh I mean you have been part of InfoQ and QCon family for so long.

03:23 It's always great to chat on these topics. So I'm looking forward to speaking with you about what's happening in the A and ML space where we currently are but more importantly where we are going right ahead of the fast pace as the AI technology innovations keep happening uh since we discussed this report last year. Before we start with the actual podcast topics I want to go over a couple of quick housekeeping items for our audience.

03:49 There are two major components to these 10 reports. One is obviously this podcast which is an opportunity for you to listen to the panel of expert practitioners and uh see how the uh AI innovations are disrupting uh the industry. The second part of the 10 report is a written article that also mentioned we can cover lot more topics in that article. So that will be available on InfoQ website along with the podcast recording that's where we actually assess the current uh technology adoption and we have an adoption uh

04:20 graph that we update every year to uh highlight you know which technologies are kind of further ahead in the adoption cycle than the other technologies. It's always a great reference for anybody who is following the technology innovations in the AI space. So definitely look for those two parts the podcast as well as article and also I uh definitely recommend to you to check out all the infoq publications.

04:43 In this podcast we will uh definitely discuss the important developments in AI technologies that took place since our last report back in August last year. But due to a limited time in this forum we won't be able to cover all the topics or even get into in-depth discussions on some of the topics. But we will try to follow up on those important topics as separate podcasts or separate articles because they are important for us to learn about.

05:09 But again, I encourage you to check out the infoq website and AI and ML and data engineering uh community which includes news items, articles, podcasts like this and presentations. So it's also worth mentioning you know with all the AI stuff happening that this podcast is not AI generated or even AI assisted. It is being recorded with real people who have real world experience.

05:31 I just want to get that disclaimer out of the way. Right. So contrary to all the predictions being made out there that AI is going to replace everything. So that being said, let's get started. It's been almost 4 years since uh CH GPD came out right back in November 2022. It's been 2 to 3 years since Rag Technologies kind of started seeing enterprise adoption and autonomous LM agents came out.

05:53 So definitely a lot of things have been happening. Agentic AI has been the kind of main dominant topic for for a long time now. So we'll definitely look at that a little bit today. We seeing more developments in the quality assurance space with the AI evaluations and hardness frameworks and all the other uh uh good topics. We we'll get into more of those details later but first topic is I would like to get a kind of quick uh take maybe 30 seconds take from each of the panelists on how do you see the current state of AI

06:22 in 2026. Chip could you start us off? So it's going absolutely crazy. So I'm tracking like all the money in and out of like AI company. So money in as in like private investment and then money out acquisitions and IPO, right? So for for many many years you'll see the charts like very similar like when the three numbers because the money in has to come out at some point.

06:42 But in recent years it's like go like this. The money in is like go like exponentially and the money out is no money out at all. It's very exciting. The amount of like money in is like I think proportional to like I guess I use as a proxy for people excitement. A lot of companies the number of company size is going down is the average like median company size going down significantly.

07:01 So that means that like people can do a lot of things that anyone can build nowadays. Anyone can have a product. So the issue is not really about like how to build things anymore like anyone can build things right but like what you build and then like how do we as a receiving end like filter through all of that is insane. And I think I know going through this kind things like winner catch all kind situations because like a few products somehow like still face and everyone just talk about them and like the rest is like

07:27 lost in the shuffle and it's like very very tricky. It's just like everyone like forcef the same kind like ideas and at the same time we have a lot of new things coming out but somehow everyone is talking about the same thing. We don't get it. Yeah. It's very very strange. >> How do we hear what are you seeing as a AI you know uh in 2026? Yeah, obviously like Chip said, there's a lot of excitements, innovations, product announcements, but I have to if you want to talk about like at the beginning of like 2026 or the end

07:56 of 2025, we're talking about the year of agents. I think we are in a phase where the AI agent stacks it is being built in a way the different layers there's a lot of progress there a lot of exciting a lot of innovations for all of us how to learn how to build AI agents and now everybody's trying to figure out how to operate them how to bring them to production so that's very exciting space from an enterprise perspective I think the other one that's I'm sure everybody is is aware of is writing code is easy now with

08:28 coding agents. So I think there's uh many exciting ideas going to be built and we all need to adjust in terms of how we build software moving forward. So I think that's another area that's happening in 2026 and we'll definitely see more excitements in 2027. >> I think we'll get into those topics a little bit more here later anyway so we'll have time.

08:50 So how about you Arthur? What do you see at a high level? Yeah, I think follow up on here. I mean, I think what we're seeing now is we're changing or shifting our perspective from the parts of the car to the car itself. I mean, you have impressive isolate capabilities in each model being developed year after year and now you're starting to realize as companies in production that we need them to work and to be dependable.

09:15 So I mean you can have a pilot of a great agent that can work beautifully. You can have created data and that's great but very good prediction that you have your data is messy. You have different permissions you have regulations. You have the system has to be affordable it has to be recoverable. So that feels like the defining test for AI that we're seeing emerging right now is not whether it can do something once but whether a team can trust it in a real workflow to solve real problems.

09:47 So that's when you see it actually being applied in companies they've been successful. How about you, Daniel? Uh, what are you seeing? >> I think the biggest change in 2026 is that AI is moving from being something that we interact to something that we delegate. No, like like Hen was saying, I think most AI applications over the last few years were based on a simple interaction.

10:12 You just ask a question and the mobile gives you an answer. right now or starting this year we are starting to see system that can plan can use tools can execute actions can do their own work and continue working for much longer period of time so I think this year is less about another jump in the model intelligence and more about turning that intelligence into actual systems >> thanks Daniel thank you for that yeah we can uh that's a good kind of segue from there right with AI agents and all the other uh innovations

10:41 happening the foundation is still are those models language models. So maybe that's where we can start our kind of main discussion today because there's always a lot of uh new developments happening there. So in 2026 kind of my uh observation is the language models have shifted from these massive size models billions of you know parameters into the smaller size models small language models right where they can uh be faster in terms of performance uh may not be as accurate but accurate enough for most of the use cases

11:09 and also we are seeing the rise of reasoning models that can think through math and logic problems before actually responding right so a lot a lot of good stuff happening so we can start with this as our main topic. Uh Arthur, I know you've been um looking at some language model topics and other frontier models. So, what do you see is happening here?

11:29 What our uh listeners should be aware of or should be learning about? >> Yeah, I think in general uh when you focus on specific models, uh we have better reasoning, have better coding, you have better multimodel abilities. They're emerging, you know, it's evolving, right? But then I'm going to go back to what I said first that if I were to pick a headline for Frontier models in 2026, I'd say that the model itself is becoming less of the whole story.

11:54 So you do have the capability you do have to worry about it of course but we're changing the question from which model is best to which model or combination of models gives us the right capability latency data boundary cost for this workload specifically this workload which to me at least is the trend seems to be a move from choosing one model to deliver matching models to workloads which makes it a system design question not a model choosing question right and I see that a time that the field is maturing because the

12:25 model stops being the whole problem and becomes the one component of the architecture. So we do have to worry about all the specific capabilities of each model but they have to work in unison together. >> Any other thoughts from anyone else on the models? So I think like model size is a little bit interesting because of the definition has changed over time because like the model size number of parameters it also depends on like how many of them are actual active at the same time right like it's a trillion parameters

12:55 model but if only like a few percentage and also another is the quantizations I do think it's like just very very exciting work on like quantization recently I feel like about a two three years ago people thought it was crazy to have like an four- bit model but now we have consistently see people running like one two bit model which is like the significant reduction in size in like we think of like as a compute requirements or like compute efficiency of like these models.

13:17 So I think it's interesting to see another trend they see from models is that of like a release cycle because like let's say that your frontier lab you have a very very strong model right you release this out in the top of every benchmark and then a few weeks later you will see they release another model much cheaper smaller like almost like equivalent but like maybe one or two% drop in performance.

13:39 So at HS this is usually a trend of like yes we have a big model and then you distill that into like a lot of other techniques uh like you doing like better mixture of expert making it more sparse quantization aggressively quantise into like smaller and a cheaper model. Yeah, there was a lot of interest in like doing fine-tuning toward the beginning of the year and there's a lot of innovation in that space with lore and all that stuff like that and all these kind of neo cloud inference providers started provide the

14:06 ability to do fine-tuning for at least the open source models. I would love to hear from the panel if you have heard successful use cases in in the enterprises they actually do fine-tuning for their specific use cases. I think there's some happening but I don't hear much about it. Obviously it takes a lot of effort to do that with the right skill sets maintenance operations doing inference the whole shebang is doable but whether that is make it reliable and in operations and all that stuff like that is still a lot of

14:40 work. So maybe things will be easier in 2027 and people do it more but that's still a question in my mind of how pervasive that is being actually done in enterprises. >> That's a good point here like how do you make the model management life cycle kind of similar to software development life cycle like how do you make it repeatable and streamlined? >> Yeah exactly.

15:00 Yeah, unlike you know traditional ML you start with data and then you train then you evaluation and then you bring it online and here you do fine-tuning. So that's still it's a lot of work but I'm sure there are specific use cases particularly perhaps these AI native company that in the domain of legal right Harvey and such I'm sure they do finetuning there but beyond that I wonder if enterprises they actually do it that's kind of my question >> I've seen a couple of companies doing that but they're very specialized in

15:37 what they do and they're very very careful about IP so they won't disclose that for few years at least I think but usually I'm seeing this basically in healthcare and finance it's concentrated there for some reason you know the reason but yeah it's happening just need the budget but it's already happening it's great then makes sense >> yeah I was just about to ask like has any of your company like fight to tuning your model ar mention is like for budget it's supposed to expensive >> I think it depends on what you're

16:10 doing how you're doing For example, when you come to open models, you know, there's a I've seen a small rush of open models and local models and people thinking that okay, it's going to be cheaper. But think about open when I have the word open. I have open weights, open licing, transparent data. I have a lot of things involved and they don't necessarily mean lower costs or commercial license or whatever.

16:35 So depending what you're doing, you need u massive data or you want to get traces out of big models to train smaller models. Thing is, I think it's still more of a craft than the artisan thing than the process, you know. So people are discovering this and although you have the the process for training models and for training things, applying that to real world problems is still a process of discovery.

17:01 So they end up spending a lot of money. I think that that comes naturally. It's R&D basically. >> Yeah. Hopefully some of these uh learnings from these companies will be shared with the community next year, next couple years as a mentioned. Yeah. I want to kind of maybe uh talk a little bit about the model u distillation topic. Uh Chip, I know you talk about this in your book titled AI engineering.

17:26 I highly recommend everybody to check out the book by Chip. Uh one of one of the very uh valuable books in the AI space. Uh so you talk about model distillation. Can you uh discuss like how it works and how it can help the overall model you know usage? >> Yeah. So I think more distillation is like extremely important. It's very exciting and I think like a lot of us today benefit from model distillations either done by like some providers or some like some application developers.

17:52 So idea is that like you have like a very big models like performs really really well but of course it's big models are very expensive to run and of course a lot of them are very slow. So like can we just like get another smaller model to like imitate a load from this big models so that it can achieve like comparable or most comparable performance but a much cheaper price.

18:11 So a lot of time we can get like so we care about something called like compute efficiency. So we usually think about okay like for this much drop in the compute efficiency like how much of the per performance we have to sacrifice and this has magic has gone going like really really really well in recent years. I think people are aggressively like developing a lot of new techniques and it's working things that didn't think would be possible even just like a few years ago.

18:38 >> Also in the models we have local versus on the cloud models right we can run the some of these models locally using or the other LM engines and also there's a multimodal models like you know for the text and images. So, anybody have any comments on those uh topics regarding these models on prim versus cloud or the multimodel models? I think regardless of the objective of defining what you're using, you need to separate a few questions what we're doing because have we seen people defining onrem versus cloud as a full

19:10 thing or really separate really bind everything. Okay, I mean it depends on where the client runs, where the model runs, where the prompts to outputs logs go, who keeps them everything is involved, right? So in the end both setups tend towards a hybrid approach. They're neither fully on prem and on the cloud. And thing is why is it important to define that for the company?

19:36 What they trying to solve? Is this a a a budget problem? Is this a architecture problem? So I mean okay you can categorize them but what for what we're doing with it? So which questions are you asking about this? It's not as simple as choosing a category and that's it. >> Thanks Abdul. Yeah, definitely a lot of lot of other stuff happening. One thing I just want to mention briefly about the jev models, right?

20:00 These are the what they call system one models. I just I just started learning about these you know I have no in-depth knowledge on this but yeah I think we will definitely share more about this in the future but uh has anybody else done any reading on this like this gel model from type safe AI that they talk about you know like very fast inference and very low cost compared to the LLMs.

20:22 >> Yeah, it's supposed to be my weekend uh experiment for Cool. Cool. >> I've been watching a lot of videos on this and recently I watched a podcast from lat space where they interviewed the founder of type safe. Very very fascinating piece of technology. One of the key things I took away from that is a different type model, but it's also extremely fast.

20:45 I think that's going to help unlock an interesting new sets of use cases. There are a bunch of interesting demos about using that kind of decision to like build UI and and other use cases that you might have not considered like hey is that a relevant use case but it's a lot of us to learn and figured out how to incorporate this but I think the key idea is at least for the founder is what they he want to do is to build a model that is for builders and the output is for software.

21:18 ware not for a human right that's the one of the key things you pointed out >> it's basically evaluates state and returns the answers is mainly for the software so we can maybe here you mentioned about inference I I heard the same thing from my own you know um experience so 2026 is being dubbed as the year of inference right so could you talk about like you know how we are shifting from training models to running AI inference and you know what does all this mean in the real world usage like what are you seeing in terms

21:48 of inference frameworks totally the inference topics I mean it's exploded since beginning 2026 obviously from one end you have all these inference stack right from VLM SG lang and so on that are helping the rest of us to spin up our own inference service in our own environment and at the same time these openw weight models are becoming more and more capable And that space is like initially not a whole lot of people how to how it works and how to operate it.

22:25 But now there's a lot more information and resources out there about how to actually use these software, how to optimize them and at the same time some of the use cases we talk about the you know financial companies where you have PIIs. So the ability to host these open waste model internally for those kind of specific use cases are very very relevant and I believe that that's going to continue to grow continue to be the case where company will do more of this moving forward now that is a lot more approachable more

23:02 easier to do. There are some companies that because they want to save cost they actually want to host internal open ways models for coding for example. So for many reasons that inference going to continue to be a very very useful very popular topic and more and more companies will start doing of this moving forward in my mind. Yeah, I think it's very exciting like for the work on inference.

23:30 Do you have like focus on specific chips or like hardware architecture to do inference on? Not so much at this point because AMD is just coming online but more and more inference provider at least the the Neo cloud they want to be able to do inference across chips you know Nvidia and AMDs and potentially near in the f future Intels and so on but at the moment still predominantly Nvidia from what I gather.

23:59 I'm curious because I think there's a trend of as you get like newer and newer chips right more powerful chips there is like many many many older generation of chips right and somebody like they like almost come for free because like a lot of fun don't use them so like what what do we do with them so I think like getting those done on like older generation of chips like making things faster could be very interesting like having like smaller I think it t like model distillations because you have like smaller cheaper

24:25 models that require less compute footprint you can fit them onto the older generation of chips and also like a lot of like upcomers for like chip because it's very hard to compete and media on the on the like on the flagship architecture but you can definitely compete at like a lower tier architecture. That's probably the next area of the competition, right?

24:42 I heard companies like Nvidia and Apple are definitely dominating the the hardware you know space right you know in terms of demand. >> Cerebras is an interesting chip player and I think we'll see more of that maybe later on this year. Ian and Chipu you mentioned in a previous conversation about agentic loops and hardness engineering and context engineering can you share maybe starting with chipu you know can you share like what's uh what do you see happening in the context engineering space >> I do think definitely

25:11 like in uh in the last couple of years Asian tech like coding use case have been dominate everything but I think like with the type set launch it shows that like actually like people have been so focused on coding there's a huge tap area for like non-coding use cases for me like coding is definitely very important but I definitely see realize it's like it's not the only thing >> yeah what what are your thoughts >> context pollution is actually the the biggest problem the problem worth emphasizing now because uh we've

25:40 seen the past year or so movements toward bigger context windows in general but they don't really solve the issue if the system cannot distinguish what's useful what's current what's authorative information from irrelevant stale or adver material. So I'm bringing something from from research here but in general more context can make an agent slower more expensive less reliable.

26:05 So the practical question would be if you have your context window regardless of the size of it what enters the context from which source with which permission for what step of the workflow so you don't have this this huge pollution it touch topics on retrieval quality source attribution rules context model in general context compression okay so I mean there's a huge huge heap of problems underneath the word context that's how I see it >> thanks for saying that Yeah, in the interest of time I do want to kind of move to

26:40 the topics. I want to focus on the agents and the evals a little bit more in this uh discussion. So we can jump into the agents topic. Daniel I know you've been uh looking into this space uh you know for a while now. So this continues to be one of the major trends in the AI space. Agents you know they have gone from like you mentioned earlier from like just the coding assistance and you know now they can act on uh several different uh apps right.

27:05 AIA agents are probably one of the biggest trends of 2026. What I find interesting is that agents are moving through several levels very quickly. So we started with personal agents helping one person. Then we start seeing agents working with teams and now companies are thinking about agents operating across entire organizations. So I think the important change is that we are moving from beyond just a chatbot model.

27:29 is that we are simply generating an answer. An agent can have goals, can use tools, can interact with software, can make decisions, can keep working until the task is complete. So I think that that changes AI from interface into something closer to an execution layer. No, h we can see this mainly in software development. Software development is probably where this transition is more visible.

27:58 So we moved from autocomplete to compilers and now we have coding agents. We have tools like clo codeex cursor replet and others that can navigate repositories can modify multiple files can run commands can execute task can instead failures and can iterate on their own. So the interesting question is no longer can AI write code. We already know that AI can write code.

28:24 I think the more interesting question is how much of the software development look can we delegate to an agent while we still maintain quality security and control that is starting to change the developers job so I think developers are spending less time producing every line of code and there are more time defining to intent giving context reviewing decisions and validating what agents can produce no so I think the recent developers surve also show how quickly agent coding has become part of every day development

28:58 workflows. So I think uh there are a lot of discussion right now on the interface disappearing the agents and enterprise integration multi- aent systems all the challenge that that that is bringing. So definitely this is something that we're going to continue seeing probably next year and and the couple of of next years all this agent and agentic phase working across organization and on a daily workflow of not only developers but in other areas of the of work.

29:35 >> Yeah, thanks Daniel. I think uh you know as the as the AI tools generate more and more code I don't think that will be any different than humans doing it. you know maybe they will be faster but in terms of the defects I think I'm sure the code will not be perfect right off the bat so how do we see these generated code how do we make sure that it's safe and secure and in compliance with the corporate standards Arthur how do we safely connect AI agents into the software development platform and SDLC process >> that's

30:04 a big question isn't it first we need to separate coding assistance from coding agents assistance would be something like an agent helping uh developer navigating a codebase. You're writing a test, drafting a patch, something like that. Agents on the other hand means the system can run comments, modify repository, open pull requests, affect deployment.

30:26 Okay. And for that we need operation design. So for operation design you need all the steps. Okay. You need authority levels, permissions, uh you need to uh audit your workflows, see what's happening there. And the thing is it's really interesting to see how the tools that work with that are emerging because the first ones are obviously based on the human flow and we just had the discussion about Jeff right we are seeing more and more tools that are realized that wait an agent is automized here okay but it's not really

31:00 a person it's a piece of software so we need to to write tools for the software to use not for a person to use we seeing this change right Now I'm curious to see how it is going to affect this operation and design for great permissions for structuring what agents can and cannot do from the coding agents perspective not from the coding system perspective.

31:23 Yeah, definitely that will be the big uh uh thing that's going to be to keep watching right thank you for that. Yeah, one thing I want to add to the discussions of agent is there's two things. One is like harness engineering. I think that an extensions of context engineering. But the thing that I see is quite interesting is relevant to a workshop that I'm going to be giving is these Asian SDKs, right?

31:44 Started out with just helping us to write the loop. But now these SDK has been expanded to provide harness engineering capabilities including memory management for context including code execution including some of the out of the box tools for doing interactual local file systems. So that's another trend. So you can see that these agent SDK from lane change or anthropic or open AI they all move in that direction of adding more capabilities as part of the harness engineering as anyone building agents moving forward

32:26 that's something to look out and leverage and there there's a whole other trends of managing these agents right so these companies are providing a runtime environment for you to host your agent whether you want to do it or not that's a question but that's what they seeing we able to build we able to build it well then how do we operate and run it at scale >> I think there is an architectural shift happening underneath all of this no so for agents to become useful inside enterprises they need to access tools data APIs

32:59 talk to other agents so all the protocols like MCP or A2A are going to become more and more different so I think the the question would be whether agent protocols become a new enterprise integration layer. So for example, today we connect applications to APIs. Tomorrow we may increasingly have agent discovery capabilities, negotiating task, uh coordinating work on top of those APIs.

33:25 So I think all of this architectural layer is also changing on the way that agents interact with each other. Anybody else have any thoughts on quickly how all this agentic AI systems are changing the team organizations team hiring and everything? >> One thing that I can say is that not developing with AI systems these days is unpractical because very expensive.

33:48 I mean people are more and more qualified. So people that are qualified to use AI they carry that on their price tag. Let's let's put it that way. So you have to use it anyway. So once you get there, how do you hire people to fit your own AI assisted workflow? I think that's the the the biggest question because again we go back to the point that we don't have your standards yet.

34:13 We're experimentating in companies with different companies, different size. It's very different to work on an AI focused company and in a bank or a repayo supplier that has its own IT department using AI for something. One common point that I've seen is people are focusing on how well a new hire can actually describe or or structure the system verbally because we go I don't like this but we go back to prompt engineering right you have to have a full understand of what you're doing because most people are not really uh

34:51 reading the code anymore I mean yeah it generates all this boiler plate and it's very hard to do a code review these days the way you're supposed to do a few years back. So with the lack of a process or tools to deal with that people are sticking to what they know or they perceive which valuable at that time and the the one common point is that new hires that can actually specify the system and get something useful out of AI something that works within their workflow.

35:20 And then you have the variance. Okay. So, uh uh if I use a workflow that's based on I don't know I have uh one API model or I have my own workflow pipeline uh and then have the specifics but the common point is that we are kind of skipping the junior level and going to the architectural design level because for a new hire to be able to really specify a system they need a certain baggage of knowledge you know.

35:47 So guys of generate the problem. How are you going to train your new hires if you are not really hiring junior people which is part of the the mess we are seeing these days but it's a big discussion. >> I do see one thing that's changing as part of the interviewing process where companies are now asking candidate to demonstrate their competency in terms of using AI tools.

36:14 is one of the new modules. Not all companies do this, but I wouldn't be surprised if more and more company will incorporate this in in the future. >> Yeah. For example, in my in my experience right now, we stop hiring junior developers. I mean, junior developers are no longer needed because of the the also the front end front end we stop hiring front end developers.

36:37 Front end is no longer needed. So what we're doing right now with the with the developer that we're hiring is that as as he said we look for people who knows how to use AI tools who know how to use the this a to to implement all the AI into the workflow and the developers are becoming more orchestrators agent just orchestrator rather than see the code and and the daily things that are happening.

37:00 So for example in our case we develop a lot of agents for our platform to do a lot of things. So the developers what they do is to make sure that they those work fine that we don't have any I don't know data lake or that we we control the expenses of the of the coding we control all these thing that we can control but the developer the the code the source code all that stuff is completely out of the of our equation right now and I think that's where the industry is moving is that it's not that developers are going to

37:32 disappear it's that the developer role is going to mainly is going to be ad orchestrator of all those agents and make sure that the agents work at they supposed to work. >> Thanks Daniel. Also here now I just want to kind of quickly cover the self learning and self-improving agents that you mentioned in the offline conversation. So can you share any thoughts on that?

37:52 How that sounds like a very fascinating topic. >> Yeah, obviously this this topic is very interesting and new at the same time. I think it's a progression right we we know how to build agents but how do we fine-tuning? How do we make it better? How to make it improve over time? There's a lot of research out there in terms of like techniques on how to build these agents that they can self-improve over time.

38:17 The startups are building products where you can give them agents and they will help fine-tune your agents. So, it's a very natural trends. I think we're still in very early days of that. But the idea is is pretty fascinating to me because if at the end of the day if you writing an agent most of your logic is going to be in the prompts in the system prompts and that you have tools and whatnot but the core kind of logic of your agents is in the prompt and now we learn like you know doing evaluations and tracing becoming

38:51 very very valuable as part of the agents and how to iterate. of I think these bits are coming together of one day we just got to be expressing what we want and then the system can help fine-tuning it and make the Asian better over time but we're still in early days but it's a very exciting space to watch. Yeah, thanks and also you shared that link the article from anthropic that is very good actually we will include as part of the references.

39:16 So it goes from uh it talks about a transformation from like person computer and then the AI solution to like agent working with workers automated basically build AI building itself right so very interesting so brought up the point about like self-improving for agent and I think the term is like a little bit like ambiguous like what is self-improving means right on the one hand you can just like okay you can tell the agent to never use rust for example right and it remembers to never use rust in the future is that

39:44 considered self improve improving or does it involve like more of like architectural change or yeah what does s proofing usually mean in um client agents? >> Yeah, good question. One example if you think about Hermes as a personal assistant that has that capability to improve over time once is know you better knows how you work and stuff like that. It's one form of self-improvement, but that's not the only form.

40:14 But that's an example of how just it's like a human once they working with you more and more, they know what you want, they want the way the style, so therefore they can be a lot more effective at helping you your task or or what you want to do right here. And it could include agents capable enough to build and train models themselves with little or no human involvement, right?

40:38 So it's the whole life cycle. The whole process is controlled by agents. >> That's another form of self. So there's many forms of self improvement. >> Something I would add here is that larger context windows don't eliminate the context problem. No. So I think agents still need to decide what information is relevant, what can be discouraged, what should persist as memory, what information is authorative.

41:03 So I think context engineering is becoming almost like a memory management for all this AI system. So giving an agent more context is not necessarily better if that context is noisy or is state or is contradictory. >> Okay. Oh yeah, let's kind of switch gears slightly. And uh Arthur, you mentioned about you are working on multi- aent systems. I mean definitely that's another very interesting area.

41:22 So whatever you can share with the audience, you know, can you tell us you know how these multi- aent systems work? What are some good use cases? Actually that's a a bit of sensible topic because when you think about multi-entian systems uh as a topic it's been there for a few decades already right before agents. So people from the academic community are seeing whatever comes with agents and after transformers and is related to the the ter multi aent systems kind of trying to reinvent things that were there in the past.

41:55 No. So my research area is on classical agent systems. So before LM which doesn't mean that you can't incorporate or use LM systems as as part of it you know but uh these days I've been working on belief for vision. So this is a big part of solving two problems actually in the AI landscape. One related to hallucination and the second one one related to information accuracy and provenence after being processed by the agent.

42:23 It's kind of between computer science and philosophy a lot of formal logic but yeah it's really interesting one thing that we've been working with I think beginning of this year end of last year formal logic based profile definition that could be used it's kind of simil similar that we can use with the concept on jab that we discussing before so have a structure input a search output and how we can make the AI not the agent I refrain from using agent but actually working from here to there.

42:58 >> Thanks a quick one back to you. Here you mentioned about uh spec driven development and software factories and I uh I've been seeing this in couple of companies they're doing this they're creating dedicated teams to kind of create this software factory model can you share more about that you know spec driven development how it works especally for those who may be new to it and uh what's happening in the space >> this probably started maybe four or five years ago and in terms of like with the coding agent now it's

43:24 you know pretty powerful but as you build a project typically you want to have a plan in advance in terms of what you want the capabilities and whatnot and and not jump to the coding agent immediate to do both. So it is kind of borrowing what we know as a good practice to do but now codified it in a form for the coding agent that you work with. So if you build a brand new like a green field project for example, you build a spec of what is it about?

43:56 What are the kind of capabilities that you want and then you kind of have a brainstorming session with your coding agent to provide the context that it needs before you actually go and build it. I think there's a lot of good practices there that people have built around this and I think most people recognize that's a good practice. I don't hear much more about it now because as people actually practicing it and the AI factory is another that's probably in the last few months of really how to infuse AI into the various

44:33 steps of your software life cycle. The typical use case is, hey, somebody raised a bug. How do you get AI to take that from beginning to end of actually take the PR, do an analysis, trying to re uh recreate the problem, come up with a fix, submit the PR, and then you have go through the whole the rest of the the steps of review and testing and so on.

45:01 that is a uh as as we all have too many things to do, too many uh tasks and I think if we can help automate that that will accelerate another dimension accelerating the software development life cycle >> the AI agents fix the bugs you know they created in the first place right so >> I want to ask this question like about like multi- aent system when I say use like mont aent system which which pattern usually go for so so For example, like is this like one orchestrator that spin up like multiple workers or like do you

45:34 have like like different agents working in parallel and then how do you and if that's the case and how do you deal with communications like across different agents working on the same kind of like complex task. >> Basically the formal definition goes back to agents working together in a social capacity in terms of their interacting with each other to solve a common problem.

45:56 Okay. And in that sense they can have a common goal, shared goals, uh distributed goals and they can use orchestrators, they can use different architectural patterns you know. So for multi- aent systems per se the definition is very broad in terms of how they can appear. But in practice what they we see is mainly related to orchestration. Okay, orchestration communication.

46:19 So since they have to work together and they have to do that in a social manner which means communicating. So FIP standards and things like that everything kind of revolves around orchestration communication. So I have different protocols for communication different architectures for orchestration a lot of work on just these two. >> Yeah. So so I'm very interested in like agent to agent communications right.

46:41 So so actually recently I use multi- aent a lot. So I have some complex task I would like get a bunch of agent to work together. So I actually built this protocol to let them like okay when you start working on a task it's a part of big system so like other agent working on the same task so we can message them to make sure that you guys go like you can co coordinate and like don't overlap.

47:03 So what happened after that is that like my task doesn't get done at all. I keep getting a lot of messages and it turns like one agent started like forwarding task to the other agent and then that agent like forward that to another agent and then none of them did the work and I'm spending like half an hour just like checking okay where does this go where I'm doing it and nobody did it.

47:23 So I feel like communication is one issue one thing but like for Asian to realize okay this task is relevant in my lane and I should do it versus like okay I just want to form it out to a bunch of other agents so you needed to be like to be aware of like what other agents are doing this what and the scope of that how how do you like manage that >> you try you don't do it you know you try to do that because thing is depending on how you're tackling problem for example one of the simplest ways of doing that is using a

47:53 blackboard kind of messaging to see what everyone's doing, what so they're just posting back and they have the the the the shared let's say context for that. But what if you have two multi- aent systems talking to each other? The problem starts that you have different ids. You can have different ID schemas for agents and you have different vocabulary.

48:11 You have different definitions for the tasks. You don't have a shared states. You don't have shared states period. And there you have research, you have know a lot of things. So for the past decades when you see all the work that was produced, they're trying to tackle specific problems in specific setups. So for example, how do you tackle the problem of one multi- aent?

48:35 So you have shared environment, share everything trying to uh you have to delegate a test that you can't do for some reason. Okay, so an agent started doing something and for some reason it was unable to keep doing that. Okay, you have to find an agent that's willing to do that. Just for that you need a setup. Are your agents benevolent? uh are do you have to to put a premium on that or or you know let's take the happy path all of them are benevolent are trying to help okay so here comes the big question how do you

49:08 define the equivalence of a task to achieving a goal so I want you to get from point A to point B okay so getting in there by car or getting there by bus it should be the same or not they have time restriction one thing that you find when you go deep and deep and deeper on that is that if you don't have a very specific shared setup, a very specific shared conditions, the difficulty of a problem increases exponentially.

49:37 You know, so one of the problems of the multi- aent systems area that we have and we know that we have is that we have a lack of standards. Okay, so we have lack standards for communicating for example have messaging standards. We do have some protocols in place but you don't have a a standard architecture or a standard state description or standard environment description for that sense.

50:01 No, that's part of the problem. You have two multi- aent systems with two different setups, two different structures, data structures, descriptions, whatever. And then you have to start tackling the partic. So how do you do that? Okay, bring me back to your problem. How do you solve that? You have to start by compartmentalizing your problem like okay this is my box.

50:22 Okay. So I'm trying to solve this problem in this very specific box with this very specific uh h conditions and setup and descriptions and good luck from here. As long as you have alien agent coming up or different multi- aent system so you have different clocks. So start over again. That's the the the usual path. Not sure which actually answers your question but it is what it is.

50:51 >> No it's a very hard problem. I'm wondering like if any of you like to hear from here then Daniel like have you used the box orchestrations ability of different models first of all like for incliners to do this task to make things faster. Have you ever used it and you have any like impressions on on them? >> I haven't done that extensively. Maybe Daniel.

51:15 >> No, regard what I do is I work with them like an ecosystem of specialized agents. So for example, you could have one agent which is responsible for coding. We can have another which is responsible for testing. We can have another which is responsible for security, another for deployment and perhaps you can have a superb base supervisor agent coordinating all of them.

51:35 No. So at that point all of the challenge start looking like a familiar to a distributed system engineering. So you have an identity, you have permissions, you have observability, you have orchestration, you have failure recovery and that communication between components between all those agents. So that's why I think agent engineering will increase becoming more like a system engineering discipline rather than just prompt engineering or or just try those those 80s individually.

52:07 >> Yeah. So, so when you mention that maybe think about like the sandbox system that we one thing I do we do see I see from the trend from like this year and last year it's like a lot of sandbox companies right and I been sandbox for like Asians so do you only like using like sandboxes for Asians for example each Asian had to work in like isolated work tree or something like that or it's like dependent on on the task >> yes for me mainly dependent on the task >> so so something I've been wondering about so so we have

52:32 like compute architecture right so AWS uh we have GCP we have like Azure and usually a company is a shop right now. Okay. Hey, I'm I'm a GCP shop. So like one of my things is like run on like Google cloud or something like that. I wonder if like will the same thing happens to like AI because right now a lot of companies have like subscriptions or like have like a contract with like multiple providers.

52:54 Do you see a future where like people would just stay with one providers maybe open air can have owners model or do you think that people would have a multi- provider system? Yes, in my opinion, I think it's going to happen the same that is happening with cloud computing. I mean, you are not tied to just one cloud provider. You work with a multi cloud providers.

53:11 So, with AI is going to happen the same. You are going to be tied to multiAI providers working on different things of your workflow, different task. But I don't I don't see just like being attached to only one provider like like you said. For example, I I work with AWS, but I have multi cloud providers working on different states of my workflow with GCP and Assure.

53:34 So with AI, I think it's going to happen the same. You can say, okay, I prefer, but in the end, we end up working with OpenAI stuff, with Grock, with any other providers. So I think that's going to happen. >> Thank you. Uh yeah, let's I would like to touch a couple more topics and we can wrap up. You know, it's been a great discussion, but I still have a couple more topics.

53:53 So I want to make sure that we share our thoughts with the audience. The first one is the evaluation frameworks. So definitely this has been a big topic this year. I even heard in one of the conference presentations that 2026 is the year of AI evaluations. So you know there are a lot of different ways of doing this harnesses and the LLM being used as judges LM as Judge right.

54:16 So how do you measure AI effectiveness AI agentic effectiveness in general? Like what kind of metrics do you see in your own experience like how do we make sure that the end to end process is being you know has enough checks and balances maybe here and can start us off on that like what what are you seeing in terms of agentic evaluation uh space >> this is a very important topic for companies that are bringing their AI agents to production particularly the one that for their customers not so much about internal ones if

54:48 you look at example from Door Dash They build an AI systems for ordering food. You have companies other companies that building for customer support agents for external users. And we know all you know we all know that LMS are not deterministics and building agents. They can they can take the wrong turn. They can make the wrong tool calls and stuff like that.

55:11 So evaluations is kind of a way to help us to make sure that the agents do the right thing for the right tasks at the first at the highest level is that and without evaluation how would you know that it's gone row or do something that's unexpected not according to your company's policies and all that stuff like that and and there's like methodology behind that and we have going to have by the way we're going to have discussions on that at Kilcon SF.

55:44 one of the experts on evaluation is going to be there and companies not only like have to do this but they have to incorporate this as one of their core steps in building AI agents and a lot of company ending up building something called evaluation harness which helped drive the whole evaluation flow from getting the data running the evaluation or tests as we all know tests right and then assessing the quality at the and and that help drive the decision whether it's a go or no go for release.

56:16 So anytime that you're building serious customerf facing agent, you have to incorporate and spend effort and time and effort of doing evaluation, right? If you build like, you know, internal agent, you're probably not going to spend a whole lot time on it. But if you building a real serious agent for your company, then definitely a must. And you see many examples across companies from LinkedIn, Door Dash, Ubers and such.

56:42 And there are many cases study on that. >> Thanks India. Definitely I think this area is going to see a lot more it all comes together right. So any other uh analysts any other thoughts from anybody else? How do you kind of evaluate your own AI you know um systems? >> Yes I think I completely agree that with the idea that this year is becoming the year of evaluations for models and agents.

57:07 So once an agent can perform an action evaluating only the model is is not enough. So you have to evalate the whole system, you have to evalate the model, the tool, the context, the agent loop, the environment. So here is where the metrics change as well. So it's not only whether the answer was correct. So is whether the agent completed the task, how much cost, how long it took, how many intervention were necessary, and whether it did something unsafe along the way.

57:35 So I think all these evaluations were just we're just starting to see how are we going to evaluate all this workflow regarding agents and regarding LM and regarding the interactions that they do in our job. >> Yeah, good point. All those metrics are more relevant now right with the overall. Yeah, one more topic you know I definitely want to um highlight in this discussion is the physical AI.

57:55 Daniel, you've been looking into this for a while. We talked about this last year as well. How do we bring the intelligence into the real world, right? So if AI agent is a software system, how do we bring that into the hardware whether they are like robots or or what now now they're being called collaborative robots coarts right then auto even like selfing cars Daniel maybe you can start us off on this like what do you see here what's happening and what we should be kind of paying attention to >> basically say how do

58:24 we bring AI into the physical world no so over the last few years we have spend the time putting intelligence into the software now We're starting to connect that intelligence to machines that can perceive and act in a physical world. So what you said, we are starting to see robotics. We're start to see autonomous vehicles, drones, and industrial systems are now part of this transition.

58:49 What I think makes physical AI fundamentally different from software agents is that mistakes have physical consequences. Now in the physical AI so one thing is that in software an agent can make a mistake generating the wrong file but a robot can move the wrong act so it's going to be a whole different level of mistake. So the reliability simulations safety verification evaluations are going to become more and more important in the physical world.

59:17 So based on that all the foundation models for robots that are happening right now, another important shift uh is that robotics are starting to benefit from more of the same foundational model approach that transform the language and vision. So instead of programming every individual behavior manually, we're increasingly seeing models that combine vision, language, perception, actions.

59:45 So is that the road can potentially understand understand instructions like I don't know pick up that object and put that on the table and translate that into a high level intent of a physical work. So as you mentioned there are a lot of projects regarding the the physical model like Nvidia like Google robots where all these multimodel models are taking language and visual information and producing action for robots.

01:00:09 So that connection between language, perception, actions is what makes the space of robotics and physical AI more interesting. So we're going to start seeing things like simulation for digital twins robots. All the things that are going to happen for open source robotics, all the things that are going to happen from AI agents to physical agents. All of this is just going to start moving faster and faster over the next year because the I mean right now the AI is here.

01:00:36 Now we have to put it in our physical world. >> Yeah, that's going to be definitely one of the applications that's going to see a lot more attention, a lot more developments, right? Whether it's manufacturing or any other physical industrial use cases any other thoughts chip you know um robotic robots and vision language models how do you see them kind of working?

01:00:58 >> Yeah, I think it's very very exciting. Definitely a lot of people say that it's the next frontier to get the models into the real world and we see a lot of progress and I think the only challenge is that it's like there are like several challenges. So people keep saying that like robotics is like cars right like LM say okay we know we have a lot of robots in the real world and we have whimos everywhere we have jokes I'm not sure is out so I haven't I have never driven one but I see the I see I see the advertising

01:01:24 for it. Uh so so we still have robots in the in the real world. So people keep telling me that like oh we know how to train massive LMS multimodel stuff. We know how to train robot like cars. We should know how to train robots to work with real world. But I don't think it's quite the same. So so one big difference between robotics models and LMS is that the challenge of backtracking right for LMS when you train thing on a virtual like on the computer if it makes a mistake you can run back right you can reset the state

01:01:53 and continue. So you can do a lot of like explorations and pick the best state. You can't really do that with robotics, right? Like you can't just get a robot. Okay, let's just squat down the stair. The robot fail falls and it breaks. You can't just run it back against the same robot as before. So it make exploration extremely extremely hard. And another challenge in data and so I was been looking into a lot of like robotic setups.

01:02:14 So a lot of them has raised a lot of money. Uh so my fleas company has raised like what like billion dollars. I don't remember off top of my head. I think like Janikun is a company we have a bunch of like ger like company on the general list of physical intelligence Sunday skilled AI which I'm like very excited about if you look into it I look looking into it and the main difference is the approach on like data so I think that people still betting on like what kind of data would help robot models to scale some trels is

01:02:44 that like sometime when it's small they don't quite work a model only work really well when they're big enough right for a long time with thousand language models like Jerry like trash and they suddenly boom like we have GD3 and GD4 is like selling everywhere. So a lot of the bet is like okay the models are not working well yet is this because the approach is wrong or is because we just do not have enough data yet.

01:03:03 So people had to make a bet on like will this kind of like architecture when this guy training algorithm will this kind data be the right approach to scale. Uh so we see a lot of this company making a big bet. So there's some companies okay we need real robotics data. So in China we have like a Chinese government is investing a lot of money into like have like robot training center right we have like humans operating like real robots and just collect a lot of this data and it's extremely expensive so a lot of people

01:03:30 were like okay we do like need data maybe you can just go with some kind of video like if human can learn from watching video can robots do it right so it was collecting a lot of these videos we have like people paying people in different countries like just like wearing like the camera on their head and collect videos so it's very interesting but There a lot of companies going to middle ground like physical intellig like I think like it's general list like Sundays they have some kind of globes so they go like okay

01:03:55 like videos are not put in for like haptic information right because a lot of time you can't judge from a video like how much force you should put into like squeezing an egg or like a strawberry so you need to view haptic information so like have some kind gloves like people have wear them and like try to manipulate object and get data to train the robots so so it's kind like the middle way because you don't have the phone robots is a lot cheaper but it's also not just videos but anyway I ramble on uh I think it's very

01:04:22 exciting space >> thanks ship yeah I know definitely we are kind of running uh shorter on the time remaining in this meeting so I know there are a lot of topics we have talked about the topics that at least that we think are definitely important to be aware of but you know there are other topics that we can probably we don't we won't have time today but I just want to quickly mention can follow up offline as separate podcasts or articles so there's AI governance platforms that are coming up And then there is the AI

01:04:47 security and regulations is another important topic. AI observability is an evolving topic as well and definitely the AI s sur and AI ops are also getting a lot of attention. So, so do you see anything else happening at very high level before we wrap up further? Do you have any comments? Yeah, I think uh we could have read relating multi- aent systems and AI engineering because uh it's not as hopeless that might have seen when I was when ship you know but uh there's a lot of things that we could uh bind together and

01:05:20 bring together to correlate to how we solve problems that we have using work that has already been developed. So that would be a great topic. >> We can start wrapping up you So this is kind of my favorite part of the podcast to request each panelist to make a prediction for next year and and we'll take a checkpoint next year and see how these predictions have kind of been realized.

01:05:39 So yeah, we can uh start off with uh Chip. What do you see? What's your prediction? If there's one thing you like to see happen or you think is going to happen in the AI space, what would it be? >> Really want to see an US robot companies that have robots ready to be bought. I feel like I know it's like very painful. I feel like in China you can just buy a robot in the US.

01:05:58 You just cannot I mean like get from an American company yet. So I really want to see the shipping. I feel like you've been waiting for like open AI and IPOs for a while. I think they delay. Very curious to see like how that's going to impact the market, right? Because like if the IPOs go well, the market can go well, but the IPOs don't go well. What implication would it have on the funding and like the AI market?

01:06:21 I would expect to see a lot of like cheaper and faster models like a type safe kind of model would be very interesting. >> Thank you. Uh how about you Daniel? What's your prediction? >> I think we're going to start seeing limitations on on AI. So for example the first is going to be the energy. So all the AI infrastructure is becoming extremely power hungry.

01:06:42 So I think the access to electricity, capacity, cooling data center locations that is going to become a biggest constraint for the growth of AI. So we may start seeing like a energy increases for AI. The second prediction that I would like to to share is is like a futuristic prediction but I think it's going to happen that all this AI infrastructure is is going to start moving beyond the the traditional data center and it's going to move into the space.

01:07:09 No. So I think uh putting AI into the space is going to be more efficiently probably it's going to take a little longer but it's going to be more efficiency than keeping the AI heat in the in the earth. And finally I think the AI competition between United States and China is going to increase increasely look like a like a technological cold war. So it's not about who has the best model anymore or who has the best chips.

01:07:37 So it it has to be with who has the manufacturer, who has the energy, who has the data center, who had the open models, the supply chains and the access to compute. So we are already seeing that semiconductor access and domestic productions are becoming a strategic issues in that relationship. So I think that for 2027 some of the biggest AI stories may actually happen outside the model themselves and it will it's going to be more about the energy the cheese the infrastructure and on the geopolitics >> and here >> just

01:08:08 have two things every AI agent will incorporate some form of the Jeff model into the AI agents and more and more of the communications going to be between agent and agents. moving forward as we figure out how to like build operate and bring all these agents to productions >> and Arthur >> I circle back to what I said at the beginning I think and I expect that the most successful AI deployments that we see will be much narrower than what we see right now in terms that rather than one agent with access to everything

01:08:45 we'll see a lot of things using smaller specialized as a agents for mixet models for well- definfined tasks okay And the big difference will be in the surrounding system. So you have trust data, limited permissions, test, a way to see not back to obsibility, but a way to see what the agent did and whether it actually helped teams can not only do that but can actually measure the cost and quality of completed task.

01:09:08 You can have ROI for tasks, not just the number of tokens of agents they use it. I think that this will be a great leap. it aligns well with what he said on Jad and what Daniel said on the shifting from away from big models but I think that's where I put my chips in. Thanks. Uh yeah, I think I I'll take a quick one minute to kind of kind of wrap up my comments.

01:09:31 Yeah, AI is a significant technology shift and as we all know by now after almost four years it is not a fad or some temporary blip on the technology radar. It is here to stay. It is one of the major technology waves in the last I don't know 20 plus years. Right? So my advice and request to our audience is we should take advantage of this technology transformation not uh be afraid of it.

01:09:54 Find ways to use AI tools to automate any workflows in your day-to-day life whether it in the office or in our personal life. I mean this is a tool that we can use to find more time to do the important things in our lives. Let the agents and the tools do the do the routine tasks. So my prediction for next year is uh definitely I agree with all of you AI will be even a bigger part of the daily lives and I always say this uh as a prediction like I would like to see whenever we kind of don't talk about AI anymore it has

01:10:24 become part of the fabric of our lives I think then we we can say we have really arrived in so I think there'll be more evolution more development but uh I think it's going to the the niche areas will be settled you know where AI will be really valuable and and where the human intelligence is still required Right. So, so thank you all for your time.

01:10:44 We took a little bit longer time but there are so many topics and uh we can go on forever but uh I think we need to wrap this up and we will follow up with any other topics later. So, thank you for joining today. Have a great one. Until next time. Thank you.