Okay. Hi everyone. Welcome to the conference. How you doing? Excellent. Usually I open these conferences with a small little talk to introduce uh you know what's going on and then give you a little update on where the state of AI engineering is and how we put together the conference for you. Uh this is a this is one of those combined talks. I'm trying to answer every single question you have about the conference about AI news about where this is all going and we'll just dive right in.

Okay. So um 3,000 of you all of you registered last minute. Uh thank you for that stress. Um I actually can quantify this. I call this the genie coefficient for uh the AI AIE organizer stress. Uh this is compared to last year. Uh it is please just buy tickets earlier like I mean you know you're going to come just just do it. Okay.

Um we also uh like to use this conference as a way to track the evolution of AI engineering. Uh that's those are the tracks for last year. We've just doubled every single track for you. Um so basically it's basically you know like double the value for whatever you uh get here and I think like uh I think this is as much concurrency as we want to do like I know I I hear that people have decision fatigue and all that uh totally but also we try to cover all of AI so deal with it.

Um we also pride ourselves in doing well by being more responsive than other conferences like Europe's and being more technical than other conferences uh like TED or whatever what have you. So we asked you what you wanted to hear about. These are the surveys. Uh we tried all sorts of things. We tried computer using agents. We tried AI and crypto. It's always a fun one. And uh but you guys told told us what you wanted and we put it in there.

Um for all for more data um we would actually like you to to finish out our survey where survey is not done. So if you want to head to that URL um we will present the results in full tomorrow. We would love all of you to to fill it out so we can get a representative sample of what you want and uh that will inform us next year. Okay.

Um you know I think the other thing about AI engineering is that we also have been innovating as engineers right we we're the first conference to have an MCP. at our first conference to have an MCP talk accepted by MCP where shout out to Sam Julian from Writer for working with us on the official chatbot and Quinn and John from Daily for working with us on the official voice bot as well as Elizabeth Triken from uh Vappy. I need to give her a shout out because she originally uh helped us uh prototype uh the the voice bot as well. So we're trying to constantly improve the experience.

Uh the other thing I think I want to emphasize as well is like these are the talks that I give like in 2023 uh the very first AIE I talked about the uh the three types of AI engineer in 2024 I talked about um how AI engineering was becoming more multi disciplinary and that's why we started the world's fair with with multiple tracks in 2025 in in New York we talked about the evolution and the focus on agent engineering so where where are we now in sort of June of 2025 Um, that's where we're going to focus on.

I think we we come a long way regardless like, you know, we people used to make fun of AI engineering and and I anticipated this. We used to be low status. People just derive GPT rappers and look at all the GPT rappers. Now all of you are rich. Um, so we're going to hear from some of these folks uh in the room. Um, and uh, thank you for sponsoring as well.

Um but uh you know I think the other thing that's also super interesting is that like you should we the consistent lesson that we hear is to not over complicate things from enthropic on the lat space podcast. Uh we hear we hear we hear from uh Eric Suns about how they beat Sweetbench with just a very simple scaffold. Uh same about deep research from Greg Brockman who you're going to hear later on um in the uh sort of closing keynotes as well as AMP code. Where's the AMP folks here? AMP amp amp I think they're probably back in the other room but um also you know there's there's a sort of emperor has no clothes like there's it's still very early fuel and I think the um AI engineers in the room like should be very encouraged by that like there's there's still a lot of alpha to mind.

Um if you watch back all the way to the start of this conference we actually compared this moment a lot to uh the time when sort of physics was in was in full bloom right this is the solve conference in 1927 when Einstein Mary Cury and all the other household names in physics all gathered together And that's what we're trying to do for this conference. We've gathered the entire the best um sort of AI engineers in the in the world um and and researchers and and and all that uh to to build and push the frontier forward.

Um the thesis is that there's this is the time this is the right time to do it. I said that two and a half years ago still true still true today. But I think like there's a very specific time when like basically what people did in in that time of the formation of an industry is that they set out all the basic ideas that then lasted for the rest of that industry. So this is the standard model in physics and there was a very specific period in time from like the 40s to the 70s where they figured it all out and the the next 50 years we haven't really changed the standard model.

So the question that I want to phrase here is what is the standard model in AI engineering right we have standard models in the rest of engineering right everyone knows ETL everyone knows MVC everyone knows CRUD everyone knows map reduce and I've used those things in like building AI applications and like it's pretty much like yes rag is there but I heard rag is dead I I don't know you guys can tell me um this day is like long long context killed rag the other day fine tuning kills rag I don't know but I I don't think I definitely don't think is the full answer.

So what other standard models might emerge to help us guide our thinking and that's really what I want to push you guys to. So uh there are a few candidates standard models and AI engineering. I'll pick out a few of these. I I don't have time to talk about all of them but definitely listen to the DSP talk from Omar later uh tomorrow.

Um so we're going to cover uh a few of these. So first is the LM OS. Uh this is one of the earliest standard standard models um basically uh from Karpavi in 2023. Um I have updated it for 2025 um for multimodality for the standard set of tools that have come out um as well as um MCP which uh is is has become the default protocol for connecting with the outside world.

Um second one would be the LN SDLC software development life cycle. Um I have two versions of this one with the intersecting concerns of all the tooling that you buy. Uh by the way this is all on the laten space blog if you want and I'll tweet out the slides so uh and it's live stream so whatever um but I think uh for me the most interesting insight and the aha moment when I was talking to anker of brain trust who's going to be keynoting tomorrow um is that you know the early parts of the SDLC is are increasingly commodity right LLM's kind of free you know um monitoring kind of free and rag kind of free obviously there's it's just free tier for all of them and you you only get start paying but like when you start to make real money from your customers is when you start to do evals and you start to add in security orchestration and do real work uh that is real hard engineering work um and I think that's those are the tracks that we've added this year um and I'm very proud to you know I guess push AI engineering along from demos into production which is what everyone always wants.

Another form of standard model is building effective agents uh our last conference we had uh Barry one of the co-authors of building effective agents from enthropic give an extremely really popular talk about this. Um I think that this is now at least the the received wisdom for how to build an agent. And I think like that's like that is one definition. Open AI has a different definition and I think we're we're contining to iterate. I think Dominic yesterday uh released another improvement on the agents SDK which builds upon the swarm concept that OpenAI is pushing.

Um um the way that I approach sort of the agent standard model has been very different. So you can refer to my talk from the previous conference on that. Um basically trying to do a descriptive u top down u model of what people use the words people use to describe agents like intent um you know control flow um memory planning and tool use. So there's all these there's all these like really really interesting things. But I think that the thing that really got me um is like I don't actually use all of that to build AI news.

Um by the way who here reads AI news? I don't know if there's like a Yeah. Oh my god, like that's half of you. Thanks. Uh uh it's it's a really good tool I built for myself and you know hopefully uh now over 70,000 people are reading along as well. Um and the thing that really got me was Sum at the last conference. Uh you know he's the lead of PyTorch and he says he reads AI news he loves it but it is not an agent. And I was like what do you mean it's not an agent? I call it an agent. You should call it an agent. Um but he's right.

Um, it's actually uh it's actually I'm going to talk a little bit about that, but like like why does it still deliver value even though it's like a workflow and like you know is that still interesting to people, right? Like why do we not brand every single track here? Voice agents uh you know like uh like workflow agents, computer use agents like why is every single track in this conference not an agent? Well, I think basically we want to deliver value instead of arguable terminology.

So the assertion that I have is that it's really about human input versus valuable um AI output and you can sort of make a mental model of this and track the ratio of this and that's more interesting than arguing about definitions of workflow versus agents. So for example in the copilot era you had sort of like a debounce input of like every few characters that you type then maybe it will do an autocomplete u in chatbt every few queries that you type it would maybe output a responding query.

Um it starts to get more interesting with the reasoning models with like a 1 to10 ratio and then obviously with like the new agents now it's like more sort of deep research notebook. Uh by the way Ryzen Martin also speaking on the product uh product management track. Um she's she's incredible on uh talking about the story of notebook LM. Um the other really interesting angle if you want to take this mental model to the stretch to stretch it is the zero to one the ambient agents with no human input. What kind of interesting uh AI output can you get? So to me that's that's more a useful discussion about input versus output than what is a workflow wise and an agent how agentic is your thing versus versus not.

Um talking about AI news uh so you know it is it is like a bunch of scripts in a in a in a trench code. Um and I realized I've written it three times. I've written it for the Discord scrape. I've written it for the Reddit scrape. I've written it for the Twitter scrape. And basically it's just it's always the same process. You scrape it. You plan. You recursively summarize. You format and you evaluate. Um and and yeah, that's the three kids in the trench coat. Um and that's really how what it is. I run it every day and like we improve it a little bit, but then I'm also running this conference.

Um so if you generalize it, that actually starts to become an interesting model for building AI intensive applications where you start to make thousands of AI calls to serve serve a particular purpose. Um so you sync you plan and and you sort of parallel process you analyze and sort of reduce that down to uh from from many to one and then you uh deliver uh deliver the contents um to the to the user and then you evaluate and to me like that conveniently forms an acronym SP AD um which is which is really nice.

There's also sort of interesting AI engineering elements that are that are fit in there. So you can process all these into a knowledge graph. you can um turn these into like structured outputs and you can generate code as well. So for example um you know chat GBT with canvas or cloud with um artifacts is a way of just delivering the output as a code artifact instead of just uh text output and I think it's like a really interesting way to think about this. So this is my mental model so far. Um I I wish I had the space to go into it but ask me later. This is what I'm developing right now.

I think what I what I would really emphasize is, you know, I think like there's all sorts of interesting ways to think about what the standard model is and whether it's useful for you in in taking your application to the next step of like how do I add more intelligence to this in in a way that's useful and not annoying. Uh, and for me, this is it.

Okay. So, I've I've thrown a bunch of standard models in here, but that's just my current hypothesis. I want you at this conference when in all your conversations with each other and with the speakers to think about what the new standard model for AI engineering is. What can everyone use to improve their applications and I guess ultimately build products that people want to use which is what Lori uh mentioned at the start. So um I'm really excited about this conference. It's so it's been such an honor and a joy to get it together for you guys and I hope you enjoy the rest of the conference. Thank you so much.