SPEAKER_02: how far away are we from agi in your mind i turn on my computer in the morning i go to work you know whatever i happen to name my assistant joe and i say hey joe what should i work on today and joe says well you know looking at your email box there's seven companies that have acute issues in your portfolio and these three probably require a phone call these four you probably need some more information based on what i've learned so i'm going to send them requests for this information and then schedule them for tomorrow and wednesday is that okay and it just like kind

SPEAKER_09: of tells me what i'm doing for the next two days when will that happen i was going to ask you how you were going to define agi and if you define agi by just what you said in that flow i think that

SPEAKER_11: we'll be at a spot where you would be able to get that within the next one to two years but what would

SPEAKER_13: you trust the recommendations this week in startups is brought to you by brave if you're building ai and search based applications train your models with the brave search api get started for free at brave.com jason arising ventures is a holding company that acquires tech startups facing setbacks arising ventures knows what founders care about because they aren't bankers they're tech founders themselves go to arisingventures.com twist today to learn more and connect with the team and linkedin jobs a business is only as strong as its people and every hire matters post your first job for free

SPEAKER_15: at linkedin.com twist hey everybody welcome back to this week in startups we're continuing our in-depth

SPEAKER_02: coverage of ai it's moving at a crazy pace and you're in for a treat today because we have david luon who is the ceo and co-founder of adept ai previously david ran research and engineering at a little organization known as open ai back in 2017 he left there to go work on large models at google

SPEAKER_00: where he focused on google brain and if you're wondering what adept ai is we're going to learn

SPEAKER_02: all about that today but basically they're building a machine learning model that can interact with everything on your computer welcome to the program david thanks so much for having me okay so you are not

SPEAKER_20: uh the highest profile person at open ai uh but you were a very uh key person maybe you could explain

SPEAKER_07: by background what you worked on at open ai because it's pretty darn impressive thanks so my time at

SPEAKER_11: opening i was uh really engaging and fun i knew a bunch of the core researchers there from just the very tiny machine learning research community from back in the day like the analogy i always like to make about how ml used to work is uh is uh imagine a world where the flat earthers toiled in obscurity for decades and then turned out to be right that's basically the story of deep learning and uh and that so that community of deep learners is actually pretty small and i joined with opening eye when it was about 35 people uh and ultimately grew it to about 135 before i left and uh primarily um folks uh in my org uh covered basic research so things like gpt2 uh and clip and dali uh all the

SPEAKER_25: way to the supercomputers and some of the larger some of the larger scale-up efforts there as well so

SPEAKER_26: when you were building those language models and maybe you could talk a little bit about what they

SPEAKER_00: were trained on i know there were collections of data sources like there's the open crawl of the web there are image libraries that were put together how was that original data set organized when you were

SPEAKER_30: in that like two and three phase so the thing about gpt2 that i think most people don't recognize as

SPEAKER_11: being two of its core contributions the first one is actually not data set related but just real quick it's this idea that every single natural language understanding task could be reframed as simply writing more text so historically people were training these models for like sentiment analysis of tweets and all stuff and you're training a model where the input is the tweet and the output is a score of as a positive or negative then you just end up with this constellation of models that all do different things but gpt2 said we can just boil this all down to one objective which is just like write more text and the next word is is it a positive or negative tweet and you get it right and the reason why that works is actually because of the data set so historically people training language models use things like common crawl as you mentioned which is like effectively like it was initially made for making open search engines right it's just all the websites you can find on the internet but most of them are trash like we looked at my colleague alec radford who's the lead author and i like looked at all the data and there would just be web pages and web pages of thousands of product codes for sony cameras and stuff like that and the core insight that alec had was that we actually live in a world where the open internet has given us these amazing tells us the weather or not an underlying web pages is smart or not and that's called reddit so what he did was he scraped reddit and found every single reddit url that linked to an outbound blog post or website or

SPEAKER_32: whatever that had more than three upvotes and said well three humans said this thing was good therefore

SPEAKER_34: it's probably pretty useful so let me so a human power search engine uh shout out mahalo was the core

SPEAKER_20: here you you used humans directing to or what you assume is humans right yeah it could be some spam or bots but generally speaking it sounds like a really good idea and you use the three vote up mechanism to filter it even further and so then you scrape those pages you build the language model

SPEAKER_11: and that's what powered gpt2 and that's why gpt2 despite today looking tiny was so smart for

SPEAKER_02: its time fascinating and then uh what about dolly and all the images i know stable diffusion there's was built off of some collection of images that a lot of researchers use so maybe you could speak to those collections of images and and how that all worked so dolly was interesting on the data set

SPEAKER_11: side there was actually not as much um there was not as much sort of uh this is not to discredit dolly to dolly at all dolly is an amazing project but but the intelligence didn't come from the data set side it actually came from this fascinating thing where um this guy aditya ramesh uh was an awesome researcher at open ai he came up with this special trick that let you predict discrete uh codes uh that correspond to images even when theoretically you should only be able to predict continuous ones so it's like a very niche fact dolly one architecture looks very different from the dolly two architecture but i actually think that the dolly one architecture was like particularly inspired and letting us do uh sophistication at that level way back in like 2019. and so where did the images come from and and how did that work i'm curious yeah just uh there was um i'm actually not sure how much i can talk about exactly where those images were sourced yeah but there was uh there was we also licensed

SPEAKER_34: some data sets which also made it easier ah so i guess there there is a little controversy there of like training of data sets what just generally speaking not talking about your time at open ai and

SPEAKER_20: specific use cases a lot of models that are being built on hugging face a lot of open source models

SPEAKER_02: they just crawl the open web and they're trained on whatever it can find yeah a lot of the open

SPEAKER_32: source models are built that way yeah it's like hackers uh trying their best to get their hands on

SPEAKER_20: whatever and if they get it in their minds totally fine let's build the model the challenge has become once it becomes a corporate entity like open ai or the stable diffusion corporation which is called

SPEAKER_07: stability stability yeah then all of a sudden the lawsuits come out and people are like hey you trained my model with this so maybe you could speak to what that means in terms of how this will all play out with regard to such such an interesting rabbit hole where that will all work out in terms of an advantage so if you do this as part of open ai uh with microsoft as a partner those are two very big targets a 90 billion dollar company a trillion plus uh valued company it's a big target for lawsuits uh and then stability obviously raised tons of money that makes it another big target but open source who are you going to target a bunch of open source you know handles that you know may or may not have built this stuff so is that going to give like this huge advantage to the open source community

SPEAKER_30: i actually think um a lot of the players in the open source community have been very buttoned up and uh and forthright about how they're handling a lot of this stuff so if you go see some of those

SPEAKER_11: models are actually explicitly licensed under a creative commons non-commercial license so there are ways in which people recognize that you know some of this data actually should not be used for commercial purposes and and the people who are then breaking the rules are then going downstream of these models that were actually in fact trained uh relatively responsibly i think what's interesting right now is we're moving towards a phase where uh where with the public internet closing down a little bit right everyone building their own wild gardens like platforms like twitter etc making it harder to for models to be trained off of plus uh this like increasing fragmentation in the internet ecosystem between the west and like the chinese internet ecosystem i actually think that um access to trainable clean data is going to be the number one problem right you make these models bigger every time you make them 2x larger you need to scale the amount of training data by a similar multiplier and people in the field are concerned will eventually run out of tokens or training data that's why people are looking into can we learn from youtube right um but ultimately the models are one fascinating property these models is that is that their maximum intelligence level the way that llms are being trained today and llm's maximum intelligence level is like really really really roughly rule of thumb the maximum intelligence level of the smartest training

SPEAKER_25: data in the corpus if you want to get better at stuff you need to be learning from smarter

SPEAKER_34: and smarter behaviors from humans got it when you look out across the data sets that are out there

SPEAKER_20: youtube pretty powerful large data set with their transcripts and you have images involved in it but as you're saying who knows the providence copyright all kinds of issues twitter filled with a lot of

SPEAKER_34: bots also kind of staccato but very much up to date so that's pretty cool uh then you have reddit which has been baking for a long time you got things like uh cora which has been baking for a long time lots of experts on it stack overflow where's the great wells if we looked at these like oil repositories who's the saudi arabia who's the uac who's the guitar who's the norway you know texas of having venezuela canadian salt flats walk us through like when you're when researchers and you know people

SPEAKER_07: are building these things and building models where they say oh this is the oil this is the diamonds

SPEAKER_11: this is the good stuff to me i think it all depends what you want to do right and uh what you know there are companies building fun chat bots like the meta characters thing uh character.ai all that stuff you're going to want very different data than if you're doing what we're doing at adapt which is how do we build like enterprise systems that help people be more productive at work right and so um so for for us at adapt the thing that we care about more than anything else is how can we learn from the smartest knowledge workers in the world and if you look at where the where the knowledge of the smartest knowledge workers in the world sits it's actually never on the public internet and because of that i think for adept to be able to build things that let any end user teach adapt a new skill at work in a very small amount of time that's the kind of stuff that we really want to be learning from long

SPEAKER_02: term yeah uh because what you do on your desktop or at your job is not published to the web it might be in slack it might be in microsoft teams it could be a notion it could be encoder it could be in a

SPEAKER_61: a google whatever that google report you're never gonna crawl that nobody would ever trust you if you

SPEAKER_63: crawled back yeah are you building the next great ai product well if so you know how expensive apis can be for their model training data training ai is pricey that's a fact we all know it so you have to try

SPEAKER_66: the brave search api yes i am talking about brave the privacy browser that i am obsessed with brave's browser has 65 million users think about how much data that drives for brave search which is the only global scale independent search index outside of big tech and that index is available to anyone with the brave search api the brave search api can power your chat bots and train your models inform answers to real-time queries and it will serve images web results and even rich text snippets the brave search api features an easy intuitive data structure and its data is populated by real human interaction not web crawlers all for a fraction of the cost of the major players it's free for up to 2 000 queries per month with paid plans for as little as a three dollar cpm that's cost per thousand so if you're building a next-gen ai app or chat bots you gotta try the brave search api get started today brave.com slash jason oh jason's brave i like it brave.com slash jason and get the browser while you add it it is awesome it's also got a vpn built in that's pretty cool why don't you show me what

SPEAKER_00: you're building at adept ai labs and thanks for the little diversion down uh history land there

SPEAKER_11: yeah for sure so first let me tell you a little bit more about what we're up to and why i'm really excited about it and then we can quickly uh flip through some some demos of a release we actually recently did last week the north star for adapt from day one actually has been that in the long term the thing that'd be the most valuable thing to build for work is an ai agent that uh does much more than reading and writing and drawing images but can actually handle for you arbitrary work tasks and workflows right and those two things are very different right like reading and writing is not the ability for you to say be able to delegate your entire like payments process to a neural network in the latter case what you really want is you want a system that knows how to use all the software you already have on your computer as if it were you and in order to get there you need to train these models that uh deeply understand not just the uh text but also the pixels on your screen and also what actions lead to what outcomes in the world and so we've been hard to work on this training this model that could do anything a human can do on a computer and we've been uh building effectively a product that enables knowledge workers to arbitrarily delegate tasks to the system here's a quick demo example um in this case uh uh let's say your response for paying invoices and you get your plumbing invoice you fire up a debt that pulls up the invoices all being done by the model right now pulls up the invoice reads the pixels in this pdf realizes what it's about stores some interesting facts about this and then pulls up quickbooks and then correctly enters who is the who is the pay right savant plumbers like how did you pay um what what category is this like and it realizes the category was never written in the pdf but it realizes a plumbing invoice so it should go into repairs and maintenance and um this task that you probably would have had to do like 10 or 20 times a day for your job you show a depth how to do it once and now every time you get a new email invoice uh you just fire up the depth and it handles this task for you it's all so where does the depth live is it in your system

SPEAKER_00: tray there or how does it you know intercept this coming in by via email because you have this invoice

SPEAKER_02: come in by email you got to get it paid you're in uh purchasing uh or accounting boom it needs to know

SPEAKER_11: so how was it just sitting there in the background running it lives as an overlay as an extension right now but we'll soon release a desktop uh overlay as well and so i think the key of this is we're not forcing you to use this brand new system it's a helper for the same workflows it's a copilot

SPEAKER_34: yeah exactly it's a copilot yeah so it's sitting gonna sit in the system tray but right now it sits

SPEAKER_11: in your browser window your browser window yep and so you can delegate arbitrary tasks to it right now i'll show you show you another example it's actually a similar variant but like one of the most common things people do is people shuttle data back and forth between system a and system b and a lot of a lot of knowledge worker jobs is just doing that like ad nauseum right um we had a we're talking to some customers who um their insurance agents have to go log on to five different software systems to be able to pull the requisite data to even get one quote done and so this next example is one where you get an uh where you get an email from uh someone who's for filing a claim and adept basically uh once you show adept how to do it once automatically fills out all and populates all the forms involved there you know what's interesting about about adept is that it's actually been a really easy way for us to start working on this like what i think is gonna be the next battlefield of ai right so far it's been about llms but i think what's what's coming up is it's going to be about multi-modality which is the ability to understand images and it's going to be about building ai agents because ai agents as defined as a model that could take a series of steps to achieve a goal is i think fairly clear to everybody in the field now the thing we have to get right to get like tremendous

SPEAKER_34: value out of these underlying smart systems yeah so agents uh if we were to explain them these were like wizards i think in the early windows days you would create a win you take a you would

SPEAKER_07: create either a wizard or a business process and all this is done offshore business process outsourcing is a big part of this yeah people send their accounting to india they send their data entry to manila whatever it happens to be and this is just taking that same concept and instead of doing it with just brute force humans uh offshored millions of them working uh in lower income or lower cost of

SPEAKER_56: living uh locations the person who's in the us doing their desktop can just have it happen in seconds

SPEAKER_30: huh it's a really easy way yeah so our first step as a company is we've been working on some of these

SPEAKER_11: capabilities that let you as a worker every day just delegate um these tedious tasks but where we're really going with this and what i'm and why uh what i'm excited about is being able to do tedious tasks is actually just a building block for what's even more valuable which is effectively having an ai teammate that you can talk to at work bounce ideas off of each other get guidance it has the same context as you do because it's uh it's it sees all the same stuff on screen and you're and uh and you're what you do at your company and all that stuff and then like helps you brainstorm and come up with the best ideas and maybe try some of them and you're like well maybe this one's a little bit better i think all of that uh lies on top of a foundation of being able to do arbitrary things

SPEAKER_00: arbitrary tedious things on your computer how close are you to having how close are you to having this in market is this like in beta somewhere are people using it yet yeah so um you know what's been

SPEAKER_11: interesting this year is that um is that the agent space has really suffered from reliability problems if you go look at the space as a whole i think there was an information article about like the agent winter or something like that it's because most of these systems the ones built on top of gpt are like 60 accurate like they work 60 of the time one out of 10 times maybe deletes half your records in salesforce and you're like i can't use this at work uh and so uh with adept we spent the whole summer unlike everybody else training our own foundation models in-house that deeply understand the pixels on screen are tuned for generating actions and that took us to a point where this fall we now have actually very reliable agent models uh once we have some custom fine-tuning data per use case so um we're excited to announce there will be later uh an announcement about um one of our really large first deployments but we also actually last week took everything we built for our enterprise customers cut out the specific fine-tuning uh that made them their stuff super accurate and then just made it into a sandbox anyone can play with and so this thing's called the depth experiments you can check it out at adept.ai experiments and uh and it's a it's a it's a super powerful first toy automation tool um funny thing is actually this morning uh someone sent me a post on upwork hiring people who were experts and using adept experiments to automate workflows so it's already getting some very

SPEAKER_20: cool attraction on the agent space now so the idea is you're going to build this platform but we as people operating businesses we'll make our own agents and for ourselves or we're going to make agents and have the ability to publish them yeah so our main as a company um we're actually very

SPEAKER_11: enterprise focused so we're currently doing larger engagements where we just come in and work with a company and figure out how the adept agents could just accelerate the knowledge work that's happening there but um this experiments framework that we made is an is an easy way for you or me or any of our friends to pick up and just try what it might be like to go automate something and then you can

SPEAKER_20: publish them and share them with people like uh gpt is doing soon yeah we'll soon be able to let you share uh so far that didn't quite make the mvp and so that becomes a business model like an app store in your mind so if i am really good at accounting i can kind of make these tools build the classifier engine for where it should live or whatever and then publish it to the web and maybe share revenue with

SPEAKER_11: you is that part of the model uh it's not the focus of our model the focus of our model is these like making enterprises really successful but um what's really interesting about what you just said though is that like we hope that in an enterprise setting you know um oftentimes like custom knowledge is locked in some people's brains right there's there's one person well at our company that's like maybe three people that know how to configure this particular infrastructural dashboard and they could just teach a depth how to do that and just publish that workflow to everybody at the company so whenever that needs to be reconfigured you just hit play on that and uh and it does it for you so i'm excited about sharing in that setting

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SPEAKER_34: your short list of ideal people to use this in the first couple years is who operations people ceos

SPEAKER_20: sales teams who are you targeting first because obviously there's many code co-pilots you're not

SPEAKER_07: going to compete in that space you're not going to beat you know github or not what we want to do

SPEAKER_34: whatever yeah so who are you targeting what jobs will become 30 40 50 faster yeah we're really

SPEAKER_11: targeting right now operations um so like all those examples we showed earlier processing invoices dealing with tracking things um shuffling data from system a to system b customer onboarding all of this like they're they're areas where you just got thousands of people who are spending their time um like instead of handling the higher level goals just handling some of this this low level flow and i think um like the reason why we really want to do this is because i mean like so many of us spend half our waking hours at work right and if that time is like reinvested not in not in more uh more manual computer process stuff uh but instead in like talking to customers or working on the next engineering project i think it's a really big unlock you can move up the stack i've been dealing

SPEAKER_20: this with my investment team and i'm saying like i wonder what low-level things we do every day we could eliminate or outsource so you know really trying to figure out how do you automate it with

SPEAKER_23: ai how do you delegate it offshore workers tend to be the work from home remote workers in lower cost

SPEAKER_00: places or how do you deprecate it so i call this my add framework that i'm literally putting my company through automate delegate deprecate look at everything you do every day and then if you do that

SPEAKER_07: well then you could call a founder on the phone and have a conversation with them how things going or you could go meet the next founder that we might invest in so there's really something here i think uh in terms of you know teams doing more with less or doing more important work uh and that is super

SPEAKER_11: exciting absolutely i think it's like you know there's uh definitely both of those but sometimes we're hearing from customers other value props i didn't even think about right like in the customer onboarding case like adapt can help cut down even time to revenue right which is a metric i never thought that the stuff we were doing would actually impact so time to revenue hey when we

SPEAKER_02: discover somebody could be a customer and when we then go do this go you know close them as a customer

SPEAKER_20: so explain to me when you pitched folks uh you raised a ton of money hundreds of millions of dollars as ai companies have been apt to do now when you pitched investors uh the argument i would have is hey there are verticalized solutions so hubspot salesforce slack notion superhuman they're all looking at hey emails coming in and superhumans looking at how do we respond to this and how do i draft your email outlook is doing that gmail's obviously doing that so and then if you're a hubspot you're going to be i've seen darmesh all day long on twitter talking about how he's automating uh hubspot it's going to go find you your next lead it's going to craft content for you so how are you going to do

SPEAKER_00: a better job versus verticalized at scale software companies and why i think this is this is the key

SPEAKER_30: question um so i think the most interesting uh pattern that we've learned from just observing lots

SPEAKER_11: and lots of people do work is that they use a million different software tools every day as part of their job right the average knowledge worker uses something like 17 different software tools and the most powerful and crucial workflows to those organizations are usually ones that span those different tools and so one uh workflows that span different tools but two custom workflows to that particular user right but even just taking a salesforce for example every cost every company salesforce deploy looks actually can often look very different from each other and so there is no one size fit all like thing you can type into a little text box be like hey i want you to go do this thing because even how you add a lead can be very different from from company to company and so the power for adept is recognizing that we should be focused on the highest value workflows and how a user could teach adept a new workflow really quickly and the second thing is that we should be focused on workflows that span uh many many different software tools so like um other examples like there's a there's a case where someone wants to use adept to go do market research every day right like on um on the the state of uh state of various different housing markets so they're pulling up like redfin and zillow and stuff like that and just running queries and populating them into a spreadsheet like whose job is it to make that happen in the verticalized side is it redfin's job

SPEAKER_25: not really is it google sheets job definitely not right those are the types of things that

SPEAKER_20: you see lots of value is that going to happen in my browser on my desktop or are you going to make me you know my researcher in the cloud and fire up a headless browser and then just have that have literally a virtual desktop on my desktop and i watch my worker my ai you know research slave go through and do analysis 24 hours a day on you know properties on zillow and redfin and put it into

SPEAKER_30: documents for me it could be either we're doing the former right now but we have had customers ask

SPEAKER_11: us hey like why don't you just go spin this up in the background and we'll just monitor it yeah so both work there's no like the hard part about getting adept to succeed is is is not in um any of the uh scaffolding bits but it's how the heck do you make reliable models like llms that read the screen decide what to act on next and do that reliably and so almost all of the our challenge comes from that

SPEAKER_26: how are you going to charge for this just be 100 bucks a person per month a thousand bucks a person

SPEAKER_11: per month how are you thinking about it yeah we're actually seeing um tremendous like most you know when we started the company um i think it was a really valuable lesson for me i always thought that i knew what this model was going to be and that we should just build for that model which is choose a couple hundred bucks per seat and like upsell people and do enterprise uh uh capabilities down the line yeah what's actually happened is from the get-go there has been so much enterprise demand and they have no exactly what use case they want to go deploy this thing in and they are willing to sign up for relatively high acb things off the bat so we've said let's just go do that and down the line when um when everything becomes more stable and mature let's pull little chunks of that out that we can then monetize uh in a more perceived sort of setting got it so you can go to a company and they

SPEAKER_34: really care about their accounting and purchase orders whatever and you can say hey just give us 250 000 a year we're going to eliminate 10 jobs or we're going to make everybody 10 times faster

SPEAKER_20: whichever however you'd like to look at it i mean it's it's all the same thing you either you don't have to hire any more people because even as you grow people will just be 30 percent more effective a year so what are the gains you're seeing per employee in the early tests what are your customers telling you they're seeing in terms of gains because you're going to be able to charge more if you can make people more productive so what are the gains like yeah we're really focused on making

SPEAKER_30: people more productive and also uh in all sorts of like side uh objectives of making people more

SPEAKER_11: productive that we didn't expect right like decreasing error rate or uh making it possible for more people at the company to go do a task right there's an interesting demo that we have where um where like uh shopify is pretty easy to use but like as an admin there are some things that like you might know how to do that like you want to make it really possible for anyone in your marketing organization to be able to tweak for example right uh even though they're not typically the people who who go who go do that and you can just teach it up how to do it once right so it's like things like that they're also really interesting side effects of a time to revenue example that i mentioned earlier that's uh

SPEAKER_32: that's the kind of stuff that we're that we're really focused on if you were to pick a number in the

David Friedberg: early tests of how what percentage more efficient people were would it be 10 a year 30 in the early tests

SPEAKER_11: so i think it depends on the on the task and we'll probably be more we're gonna we'll probably publish a case study at some point on this where we'll have more details but like we're hearing things like a workflow that might take somewhere an hour and a half goes down to 30 minutes for example got it okay it's still very much something where we like our philosophy for how to get the general intelligence involves agents but it also involves a lot of human oversight and um and so we've

SPEAKER_25: been the whole time we only basically build human loop systems where you know what the models are doing

SPEAKER_20: i think this is a a fine way to look at it businesses are growing 30 year over year 20 year over year like uber grows or microsoft whatever they grow 10 20 30 year over year and their teams now this last year and i think a lot of it had to do with ai and also people getting fit and maybe not hiring ahead their teams went down in size and the revenue still went up 30 so i think what's going to happen um is it's not job destruction uh or elimination i mean sure some jobs will go away because of ai that would probably be a good thing because there would be menial tasks that are

SPEAKER_00: arduous but you can have the same team size and instead of having to add a thousand people every time you add i don't know uh 10 million dollars in revenue or 100 million dollars in revenue for some

SPEAKER_02: big group you're going to be able to do because the whole team can be 30 faster 50 faster at these

SPEAKER_00: repetitive tests maybe you don't have to add anybody so the company stays the same size but

SPEAKER_25: yet can do more is that your thesis as well or i think giving yeah giving knowledge workers and companies lots of leverage through these systems is really the focus all right congrats to the team at

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SPEAKER_138: do it faster post your job for free at linkedin.com twist it's linkedin.com t-w-i-s-t to post your first job free terms and conditions to apply what will this look like in five years if you're successful

SPEAKER_30: so this is the part that i'm i'm most excited about i think as we were talking about earlier the

SPEAKER_11: first part of what we're doing is is enabling you to delegate things you don't want to do right but i think where this really heads is as these agents become smarter and smarter um and become better and better handling higher level things right maybe right now it's like hey like here are the steps required to do this invoice processing thing but maybe in like two or three years instead it's like i want to think about what i want to do in this part of the business let's figure it out and let's plan some scenarios together like that is the interaction model that i think is going to be extremely powerful and um you know my personal background was i was always working on agi right um at open ai that was our north star at google uh when i was leading a large models effort there we were really thinking about how do we scale up these underlying models and combine them with the other things we need to do to get smarter systems with adept and we are building a super commercial company with a product that enterprises use but the reason why we do this every day is because we actually think this particular path of building ai agents that can do smarter and smarter things for you at work that are interacting with and learning from the world's best knowledge workers and learning not just how to read and write but the consequences of doing things that have reward is actually the critical path for getting to general intelligence systems in sort of the most the most predictable way and so what i expect to see from the adept product is that the abstraction level where you can ask it to do will continue to get higher every year absolutely fascinating and

SPEAKER_00: how far away are we from agi in your mind i turn on my computer in the morning i go to work

SPEAKER_02: and you know whatever i happen to name my assistant joe and i say hey joe uh what should i work on today and joe says well you know looking at your email box sounds like you know there's seven companies that have acute issues in your portfolio and these three probably require a phone call these four you'd probably need some more information based on what i've learned so i'm going to send them requests for this information and then schedule them uh for tomorrow and wednesday is that okay boom and it

SPEAKER_07: just like kind of tells me what i'm doing for the next two days when when will that happen you had to

SPEAKER_11: pick a date so um that particular flow you just said i was going to ask you how you were going to define agi and if you define agi by just what you said in that flow um i think that we'll be at a spot where you would be able to get that within the next one to two years but what would you trust the recommendations

SPEAKER_07: i'm not sure we'll get let me say recommendations being as good as an mba from a school and someone who gets paid 100 or 150k a year yeah in other words if you know to to make this like a classic test i wouldn't be able to tell the difference between its requests and an mba who is a chief of staff so you know 150 000 a year chief of staff who crushes it would give i wouldn't be able to tell yeah the ai

SPEAKER_11: from the chief of staff i think uh with that particular flow you just said i think definitely less than five years and i think five years is conservative wow so the the this idea of a chief

SPEAKER_02: of staff being able to watch an executive work and fill in all that connective tissue and advise them

SPEAKER_05: what you know where their attention needs to be just done completely by ai i think giving you a couple

SPEAKER_11: a couple of suggestions of which maybe one out of three or two out of three hits i think that's yeah

SPEAKER_02: some five years away yeah i mean the chief of staff might give you five suggestions and you say like okay we're gonna go with these three and the this one i would never do but thank you for the suggestion

SPEAKER_05: here's a learning thing and this one yeah we'll consider it but let's put it on the not right now

SPEAKER_155: list yeah pretty amazing yeah i think it's a pace of progress you know i think right now the field is

SPEAKER_11: still split between people that are like wow like i see how this stuff is going to keep compounding and then people who are like well um just because the last three years has been crazy it doesn't mean the next three years will be crazy kill abilities are slowing down like models aren't going to get too much smarter anytime soon um i think the first group is correct i think we're going to still see tremendous

SPEAKER_132: progress over the next couple years awesome well this has been absolutely amazing this was really fun thanks for thanks for inviting me on this like great questions and yeah i mean i've been talking to

SPEAKER_00: everybody and it's really interesting because i i've gone down the agent rabbit hole a little bit and watching the desktop rabbit hole and i think you're really on to something and thank you i just i think it's going to just work and then the question is what can you charge for it and you know what which

SPEAKER_02: verticals can you actually carve a niche i do like the answer to your question the answer to the question

SPEAKER_20: of siloed versus across your entire desktop i think there will be excellence inside of superhuman or other apps that'll just be amazing you open up your notion here's what you missed but then there's going to be a moment where it's like oh here's what you missed in notion here's what you missed in slack here's what you missed in salesforce i took those three here's what you missed moments yeah and pulled them together and i pulled them together here's what you missed in totality exactly

SPEAKER_25: they won't be substitutes for each other they will actually both coexist very happily and i think the

SPEAKER_11: key with the agents thing is just getting them to actually be reliable and that's like i think that's that's the key advantage that like that we're really trying to run at is uh is you got to control the

SPEAKER_165: whole model uh stack to do that all right everybody we'll see you next time on this week's startups bye