Meta AI Chief Wang on Winning the Race in AI

[00:00:00] Speaker 1: it was almost exactly a year ago that you started at meta leading the ai team and i thought maybe you could set the scene for us just a little bit i i'm wondering if you can let us know where you think meta is as an ai company today versus where it was a year ago specifically like what's the reputation of meta as an ai company today versus a year ago when you got there um yeah i mean i [00:00:24] Speaker 2: think it's been a very exciting year for um for meta and ai i mean i think when um you know roughly a year ago a little more than a year ago meta released llama 4 and while that was still an exciting release i think you know it wasn't quite on the trajectory that meta needed to be able to continue building a lot of the products and experiences that it seek to build and so you know we've been hard at work over the past year since i joined and since starting meta super intelligence labs you know we've been undergoing an entire process of building a new scaling ladder for our models uh developing new sets of infrastructure uh and and new research to be able to power sort of a new series and family of models and you know back in april we released the very first fruits of that labor you know the new spark models and an update to meta ai the reception to all of that was incredibly positive it was even better than frankly we'd expected internally we saw incredible gains and usage of meta ai it was you know at the top of many of the app stores and um you know we're sort of working on our even larger models today and uh and are excited about what we'll be able to demonstrate to the world we clearly you know are we're on a very exciting and fast trajectory and we're excited to continue showing the world what we produce um i think that uh you know the ai industry has gotten very uh hot and competitive obviously in the past year as well and you know we take that very seriously but we're really excited [00:01:57] Speaker 1: yeah obviously the goal you're spending in the same way that an open ai anthropic others are spending do you feel like you guys are in that same tier is is meta you know is it open ai anthropic meta at this point or do you feel like there's still a bit of a gap because i'd say certainly a year ago [00:02:13] Speaker 2: that was the perception yeah we don't i mean the new spark model that we released is not at the tier of the leading frontier models um but we believe it's a very exciting data point on the trajectory and we expect the upcoming models we release to be quite competitive with the leading models in the world [00:02:31] Speaker 1: yeah you called new spark um an appetizer an appetizer model uh to to build that metaphor out when does the entree model get here you know will it be at that tier that that you guys ultimately want to be at [00:02:46] Speaker 2: yeah we are uh we are in process of cooking the uh the whole conversation is just going to be this this back and forth yeah yeah no no i mean we're we're cooking it um we're excited to show it to the world once it's ready um we're seeing very uh exciting and promising results in the process of training it um right now so uh we're quite excited about it and and i think there's a you know overall we built the entire research effort around predictable scaling so the so the entire belief of our overall research effort was you know um that in many ways the central belief behind the current modern ai boom is that as you scale these models you will see um get incredible results and get predictable levels of increased capability and so um you know new spark it was an early data point on that scaling ladder for us the next models we release will be uh an even greater point on that on the scaling curves and we are really excited to show the world what we'll we'll be able to produce and what obviously you went from [00:03:46] Speaker 1: basically rebuilding the team rebuilding the lab to this new spark model in a very short amount of time what is the biggest barrier from getting to that um the appetizer to the next level is it uh i assume it's not resources you guys are spending a ton of money on this on this effort um is it just simply time is it talent like what's going to bring your models to that that frontier yeah it's um you know we we've [00:04:12] Speaker 2: talked about some of this in some of our our public uh blog posts and whatnot but it's about continuing to scale the data the uh compute going into the models um as well as continue to scale with research so continue to uh drive advances in um you know underlying you know underlying research breakthroughs to continue driving forward the progress of the models um and building infrastructure to support all this you know it is um this is in many ways a year where uh all the labs are dramatically scaling up their models and we are on we think a much faster trajectory to do so because obviously we've been doing all this work over the course of the past year um but yeah we need to build the infrastructure we need to scale the data scale the compute um train these large models and show them to the world yeah [00:04:58] Speaker 1: i want to talk about model strategy a little bit before you got to meta everything was open source that was definitely the overarching strategy new spark model is not open source um i believe i heard you on a prior interview basically say that as you guys were testing it it didn't feel safe to open source can you go deeper on sort of what you mean by that um and how you made that decision yeah so one of the [00:05:23] Speaker 2: things that we did when um as part of meta superintelligence labs is we updated our what we call our advanced ai scaling framework which is really our view of you know what are the risks that we see in developing these very powerful models and how do we want to handle those risks as we see them in early testing and we publish a lot of what we saw in the process of training new spark in our preparedness report and some of the things that we saw is that it actually triggered some high risk areas uh in the course of early training particularly around bio risk um but also a number of the risks were were elevated and um you know this is something i think the entire industry has seen as the models have improved pretty dramatically over the past year so we certainly aren't the only ones to see a host of these risks you know show up in in as we scaled up the models and as we sort of kept pushing the frontier of research but um we saw these risks triggered and we realized you know i think fundamentally those um when we launch a model like new spark in a product we have a lot of ways to mitigate some of these risks and ensure that we're able to launch it in a safe and responsible way um it's much harder to do that when you open source a model and you know people can use that model in all sorts of contexts that we may not have full understanding of so we're in the process right now of developing um models that we believe are fit and safe to be open source while still maintaining as much of the performance capabilities as possible [00:06:51] Speaker 1: so you will still do open source it sounds like llama though is not the brand or the the pillar that you're going to do is everything open source going to be muse spark or adjacent uh you know we uh we [00:07:06] Speaker 2: have uh exciting debates about branding internally and uh nothing to share right now but uh yeah okay um [00:07:15] Speaker 1: the the big models that are coming that you've hinted at um give us a general sense if you can obviously you know each model each company is is perhaps known for certain things do you feel as though you're moving in a direction where meta's models are going to be known for you know best in class at x versus y like what are you hoping to accomplish with with what you guys come out with next [00:07:39] Speaker 2: yeah so so already in new spark some areas where we were really impressed by the capabilities um even those again like a much smaller model than ultimately we um we intend to train were around multi-modality capability so its ability to handle images video audio and that's obviously very important and critical for meta's business um also its capabilities and health were really impressive um and that that was very exciting for us you know health is an area that we view as really critical as we scale these models out to billions and millions of people all around the world um and then also a lot of the early results we saw in the ability of the model to create you know vibe code and create little games or artifacts or whatnot were very powerful so um we are doubling down on some of these and and continuing to invest into the agentic capabilities of the models so we're really excited for the upcoming models we release to be very very um uh capable agents uh paired with a lot of these other strengths around multi-modality around health and many others and ultimately what we're really excited to build for the world is our the best personal agents for uh everybody around the world as much as possible [00:08:50] Speaker 1: i want to get to agents in just a second because you you just made some news on agents actually yesterday you have other stuff in the works that we can talk about but before we pivot off safety real quick i do want to ask a question just about china um you've been you know you've talked publicly about the risks um and the threat of ai coming out of china you guys have also trained on some chinese open source models i'm just wondering can you give us a sense of like how you view china right now in the in the you know through the lens of ai is it a threat in the way that we've heard historically do you feel [00:09:22] Speaker 2: like that's changed yeah i think that um i think it is incredibly important for the united states to lead on technology and the uh economic benefits uh that can be created from ai i think this is this is very very critical if you look at the history of civilization you know technological advances are um very important for for for uh countries or civilizations to adapt to and and be able to adopt um and that really defines you know the really truly the course of history over uh over long arcs so i think it's very important that that the united states is able to lead on ai um and uh that's that's a huge part of our focus at meta as well as ensuring that we are able to contribute to the united states leading where do you think we are right now as a country is the us leading uh i think right now the us is leading yes okay and i think it's um you know this is one of these situations where you you know it's important for us always to track progress from many other countries but especially china you know be very thoughtful and understand exactly um what's happening within each country and what are the reasons those things are happening but [00:10:37] Speaker 1: i i think right now we're ahead and what sorry i said i was gonna get the agents but one more uh what could put that at risk what could put that lead at risk what's the most threatening um thing to stop [00:10:49] Speaker 2: that um that's a good question i mean ultimately i think we are in a phase where um the the research advancements industry-wide that we're seeing from continuing to scale these models apply more compute apply more data to these models are just um incredibly uh exciting and you know in some ways the the progress and pace of research today is nearly miraculous and so um i think that it's important that we're able to continue this pace of progress that we're able to continue um you know being able to [00:11:23] Speaker 1: uh continue scaling these models yeah um agents now finally um you guys just yesterday i believe it was announced a business agent um so advertisers can use this to you know interact with customers i presume eventually help even um develop ad campaigns things like that but you're also developing a consumer agent talk me through your vision for how ultimately agents will reach all of us like i i guess i'm wondering am is an agent going to be similar to like my email address where i have one core agent and maybe a secondary agent or is it going to be like the apps on my phone where i have one agent for every single task in my life like what do you envision we're going to be using as a society yeah i you [00:12:06] Speaker 2: know we ultimately think it'll probably land somewhere in between those i think you know we really believe that people are probably going to have one maybe two maybe a small handful of of agents that they rely on and maybe they have a personal agent that's focused on things like their health and maintaining their personal relationships and you know helping them be a better parent and be better with their friends and family um and then you know perhaps they use that same agent in their work lives especially if they're working a small business or they're an entrepreneur or um or um you know uh you know working within a smaller organization and then you know maybe there's worlds where if you work within a larger enterprise or a larger company then these become bifurcated and separated not too much unlike email let's say or plenty of other uh you know key technologies that we use from day-to-day basis so um yeah we think that ultimately uh it should be you know agents will be something that are that become deeply personal um and should be things where over time you find yourself being able to rely on them more and more and more for um more and more of your personal life more and more of your work life and that'll be a process that you know all society goes through it together do you feel that meta in [00:13:21] Speaker 1: particular and again a lot of this happened before you got there but long history of privacy related issues are people going to be willing to you know trust a meta agent with the personal tasks of their life [00:13:33] Speaker 2: that that you're describing um yeah i think that this is like one of the most important societal question for agents writ large i mean i think that um there's incredible uh amount of innovation and technology that's we built out on things like agentic safety um you know uh ensuring that these agents are respectful to your privacy ensuring that they're respectful of of your boundaries um and continuing to design products in a way that are able to support that so uh this is definitely something that we're taking very very seriously and we're being quite thoughtful about um and ultimately we're excited to you know show the world what we've built but um but yeah we we think that this is not even just a meta problem this is an industry-wide problem as we build more and more powerful agents i think it it is a redefinition of you know humans relationship with technology in many ways and that's something that um you know we're all going to have to think through and work through together how soon will we see a meta consumer agent um we are actively cooking it cooking the entree yeah but no this is like i think this is one of the things that was very exciting for us internally about the new spark launch and metai is that you know we even when those launched we were cooking things internally both the larger models as well as um as well as some of these products that you referred to that were if anything more excited about than what we came out with in in april okay and how are you [00:14:56] Speaker 1: using agents right now your boss mark zuckerberg it's been reported he has like a zuck bot essentially uh or various versions of of agents that he's tasking some of his ceo duties to is there an alex bot that's that's doing part of your job right now while we're on stage um well i definitely use agents to support [00:15:16] Speaker 2: and help me um in my work a lot i think that in many ways um you know being a leader within a company is really about how well are you able to you know understand everything that's happening at the company to the best you can and help you know support your team and being able to continue to execute better and better so i think there's all sorts of things that ages can do to help you there but i think the uses of agents that are probably most exciting to me are the ones where i use are the ways i use them in my personal life so you know i use an agent to help me be healthier um and i use an agent to help me keep in touch with my friends and like ensure that i maintain those relationships and i think these are use cases where they're like very notably different from the world where i didn't use an agent at all like these are things that i think are like hard have been hard historically for me to like stay on top of you know both my health as well as um keeping in touch with all my friends and having an agent that's there to help support you do a good job of those things has been [00:16:17] Speaker 1: pretty transformation yeah um somehow we only have a couple minutes left but i wanted i want to ask some sort of bigger picture questions about ai and society um but but i actually want to start at meta right so your team is getting an immense investment um you guys are investing hundreds of billions of dollars in ai writ large at the same time there were layoffs uh at the end of the last month so there are people and and some of the framing is hey this is to offset that as the person in charge of ai i'm just wondering how do you deal with that reality right that that you are working on a product that you're excited about but at the same time you know the company is saying hey this is costing jobs as well like how does that make you feel how do you deal with that internally i'm just [00:16:58] Speaker 2: wondering how you you know deal with that reality it's incredibly difficult to say goodbye to teammates and i think it's you know it's no um i think it's very well known that this is this is a challenging thing to go through as a team and something that you know is uh is important to acknowledge you know we are ultimately really excited about the progress that we're making in ai and the products that we're building and um you know we're excited to bring those to the world um but yeah i think that there's um you know running a large company is very complex and um you know we're working through a lot of those issues but um you know we don't take any of it lightly but you know at the end of the day we're [00:17:43] Speaker 1: excited about what we're building i think job loss in general is probably like one of the biggest fears with ai um what's your view on that is is if ai goes the way that everyone envisions and we reach super intelligence um is there a world in which that can operate and live alongside all of us staying employed [00:18:04] Speaker 2: um i mean i think it's it's something that we should pay a lot of attention to and i think that we should you know track closely and try to understand what the impacts are i think one thing though that we never you know i think rarely talk about but is also happening that's very exciting is that ai is enabling the creation of more businesses than ever before in the world and you know we see this in our data i'm sure many companies see this in their data there are more new companies being started today be like through use of ai tools than ever before and that those numbers are only growing and i expect as ai tools become more and more powerful we'll see more entrepreneurs more small businesses being started and so um i think there's you know it's the economy is a complex you know machine and one of the things that we see within our data today is that there are more smaller small businesses being started there are more entrepreneurs um there's more opportunity for entrepreneurs and so um you know uh and we're really excited about that we're excited about supporting small businesses [00:19:03] Speaker 1: throughout the world is it the kind of thing that you feel we saw the trump executive order just this week where the administration wants to sort of review some of these models before they're they're released i'm curious what you thought of that first of all but two is there another type of regulation that you think actually might be helpful to prevent you know job loss or prevent um a world in which ai is is taking priority over the humans [00:19:29] Speaker 2: yeah i think well i mean first of all i think it's it's um this is a really important and powerful technology and so i think um it's it's great that the administration is you know deeply considering uh what how we should be thinking about this technology how we should think about uh responsible deployment and and what all that entails so i think that's um broadly speaking i think it's been really great that this has been an issue that the administration has been really involved on and very thoughtful of and um you don't think it slows innovation to have a review process like that um well i think i think it's always a balance you know regulation is notoriously difficult to get right in almost all contexts but um but i think that you know what we what we're seeing from these models is just that they are becoming dramatically more capable and i think it's important that we're thoughtful about how we deploy them ultimately okay