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 Request for this information and then Schedule them for tomorrow and Wednesday Is that okay boom and it just like Kind of tells me what I'm doing for the Next three days when 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 we'll be at a spot Where you would be able to get that Within the next 1 to two years but what Would 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 ra.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 arising ventures.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 at linkedin.com twist Hey everybody welcome back to this week In startups we're continuing our In-depth coverage of AI it's moving out A crazy pace and you're in for a treat Today because we have David Luan 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 where he focused on uh Google Brain and if you're wondering what Adept AI is we're going to learn 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 uh the highest profile person at Open aai uh but you are a very uh key Person maybe you could explain by Background what you worked on at open Aai because it's pretty darn impressive Thanks so my time at open aai was uh Really engaging and fun I knew a bunch Of the uh core researchers there from Just the very tiny machine learning
Research Community from back in the day Like the ology I always like to make About how ml used to work is uh is 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 Actually pretty small and I joined 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 orc uh covered basic research So things like gpt2 uh and clip and doly Uh all way to the supercomputers and Some of the larger some of the larger Scaleup efforts there as well so when You were building those language models Maybe you could talk a little bit about What they 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 in that like two And three phase so the thing about gpt2 That I think most people don't recognize As being two of its core cont Attributions the first one is actually Not data s 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 with the input is the tweet and The output is a score if it's it 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 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 the um my colleague Alec Radford who was a lead author and I like Looked at all the data and there would Just be web pages and web pages of Thousands of like 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 as to whether or not an underlying Web page is is smart or not and that's Called Reddit so what he did was he Scraped Reddit and found every single Reddit URL uh that linked to an outbound
Blog post or website or whatever that Had more than three upvotes and said Well three humans said this thing was Good therefore it's probably pretty Useful so then we so human powered Search engine shout out Mahalo was the Core here you you used humans directing To or what you assume as humans right Yeah 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 a language model and That's what powered gpt2 and that's why Gp2 despite today looking tiny was so Smart for its time fascina 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 Lot of researchers use so maybe you Could speak to those collections of Images and and how that all worked so Doly was interesting on the data set 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's 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 D Remesh it was an awesome researcher at Open aai he came up with this special Trick that let you predict discret 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 the dolly 1 Architecture looks very different from The dolly 2 architecture but I actually Say think that the dolly 1 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 There was we also licensed 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 aai And specific use cases a lot of models That are being built on hugging face a Lot of Open Source models they just Crawl the open web and they're trained On whatever it can find yeah a lot of The open source models are built that Way yeah it's like hackers uh trying Their best to get their hands on 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 stability stability y 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 it's 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 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 I Actually think um a lot of the players In the open source Community have been Very buttoned up and uh forthright about How they're handling a lot of this stuff So if you go see some of those models Are actually explicitly licensed under a Creative common 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 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 wal Gardens Like a plat forms 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 of these models Is that is that their maximum Intelligence level the way that llms are Being trained today and lm's maximum Intelligence level is like really really Really roughly rule of thumb the maximum Intelligence level of the smartest Training data in the Corpus if you want To get better at stuff you need to be Learning from smarter and smarter Behaviors from humans got it when you
Look out across the data sets that are Out there YouTube pretty power F large Data set with the 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 bots also kind of Staccato but very much up toate so That's pretty cool uh then you have Reddit which has been baking for a long Time you got things like uh Kora which Has been baking for a long time with Lots of experts on it stack Overflow Where's the great Wells if we looked at These like oil repositories is who's the Saudi Arabia who's the U thear 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 are Building these things and building Models where they say o this is the oil This is the diamonds 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 Character. a all that stuff you're going To want very different data than if You're doing what we're doing at ad Depth 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 a depth 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 go 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 or Teach Adept 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 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 in notion it could be In Koda it could be in a a Google Whatever they Google rep you're never Going to crawl that nobody would ever Trust you if you crawled that 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 the brave Search API yes I am talking about Brave The Privacy browser that I am obsessed With Braves 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 form 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 $3 CPM that's Cost per thousand so if you're building An X gen app or chat Bots you got to try The brave search API get started today Brave.com Jason oh Jason's Brave I like It brave.com Jason and get the browser While you at it it is awesome it's also Got a VPN built in that's pretty cool Why don't you show me what you're Building at a de AI labs and thanks for The little diversion down uh history Lane there fascinating 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 Northstar for dep uh from Day one actually has been that in the Long term the thing that' 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 work flows 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 let's say your respons for Paying invoices and you get your Plumbing invoice you fire up a debt that Pulls up the invoice is 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 quick books 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 Adep how to do it once and now every Time you get a new email invoice uh you Just fire up the dept and it handles This task for you it's all so where does The dept live is it in your system tray There or how how does it you know Intercept this coming in by via email Because you have this invoice come in Via email you got to get it paid y You're in uh purchasing uh or accounting Boom it needs to know 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 you Co-pilot yeah exactly it's a co-pilot Yeah so it's sitting going to sit in the System tray but right now it sits in Your browser window your browser window Y 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 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 of 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 going to be The next battlefield of AI right right So far it's been about llms but I think What's what's coming up is it's going to Be about multimodality 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 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 w you take a you Would create either a wizard or a Business process and all of 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 Offshor millions of them working uh in Lower income or lower cost of living uh Locations the person who's in the US Doing their desktop can just have it Happen in seconds huh it's a really easy Way yeah so our first step as a company Is we've been working on some of these 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 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's it sees all The same stuff on screen your and uh and Your 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 Lies on top of a foundation of being Able to do arbitrary things arbitrate Tedious things on your computer how Close 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 Interesting this year is that um is that The agent Space is really suffer Ed 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 the depth 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 have actually very reliable agent Models uh once we have some custom Fine-tuning data for 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 is called adep experiments You can check it out at ad dep. 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 in using Adept experiments to Automate workflows so it's it's already Getting some very cool attraction on on The agent space now so the idea is in 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 To publish them yeah so our main as a Company um we're actually very Enterprise focused so we're currently Doing larger engagements where we just Come in and work with our 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 publish them And share them with people like uh CH
Gbt is doing H soon yeah we'll soon be Able 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 uh 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 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 often Times like custom is locked in some People's brains right like there there's One person well at our company there's Like maybe three people that know how to Configure this particular Infrastructural dashboard and they could Just teach AEP 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 and it does it for you so I'm Excited about sharing in that setting All right you've heard me talk about Arising Ventures a bunch recently they Are a holding company they acquire Tech Startups that uh you know are facing Some headwinds some setbacks and they
Give these startups a second chance at Life which is awesome so if you're going Through some tough times right now and You're trying to get back on solid Footing well reach out to the team at AR Rising Ventures could be just what your Startup needs to get back on track They've helped companies like upcounsel Up counil they took from burning a Million a month and shrinking to Profitable and growing and Jive where They relaunched a shutdown company went From zero to 1 million in ARR in just 5 Months what a save in fact two saves Listen arising Ventures knows what Founders care about because they're not Bankers they are Tech Founders Themselves and they're here to help your Startup get back on track learn how Arising Ventures can help give your Company New Life by visiting arising Ventures.com twist today to learn more And connect with the team that's arising Ventures.com Twist your short list of Ideal people to Use this in the first couple years is Who operations people CEOs teams who are You targeting first cuz obviously There's many code co-pilots you're not Going to compete in that space you're Not going to beat you know GitHub or Whatever so who are you targeting what Jobs will become 30 40 50% faster yeah We're really 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 there're areas where you just got Thousand of people who are spending Their time um like instead of handling The higher level goals just handling Some of this this slow 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 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 H how do you Automate it with AI how do you delegate It offshore workers tend to be the work From home remote workers in lower cost 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 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 yeah 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 like super 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 Adept 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 Discover somebody could be a customer And when we then go do this go you know Close them as a customer so explain to Me when you pitched folks uh youve 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 Superhuman is 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 daresh all day long on Twitter Talking about how he's automating a 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 a Better job versus verticalized ATS scale Software companies and why yeah I think This is this is the key question um so I Think the most interesting uh pattern That we've learned from just observing Lots and lots of people do work is that They use a million different software Tools every day as part of their job Right they 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 has span Different tools but two custom workflows To that particular user right but even Just taking Salesforce for example every C every company Salesforce deploy looks Actually can often look very different From each other and so there is no one- Siiz 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 an a
User could teach Adept a new workflow Really quickly and the second thing is That we should be focused on workflows That span of many many different Software tools So like um other examples Like there's a there's a case where Someone wants to use AEP 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 red finin 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 vertical eyes side is it red Fin's job not really is it Google Sheets Job definitely not right those are the Types of things that see lots of value In 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 Yeah on you know properties on Zillow And redin and put it into documents for Me it could be either we're doing the Former right now but we have had Customers ask 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 AEP 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 how are you Going to charge for this it's be 100 Bucks a person per month a thousand Bucks a person 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 This 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 what's actually happened is from The get-go there has been so much Enterprise demand and they have know Exactly what use case they want to go Deploy this thing in and they are Willing to sign up for relatively High ACV 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 proceed sort of Setting got it so you can go to a Company and they 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 whichever however you'd Like to look at it I mean it's it's the Same thing you either you don't have to Hire any more people because even as you Grow people will just be 30% more Effective a year so what are the gains You're seeing per employee in the early Tests what are what your customers Telling you they're seeing in terms of Gains because you're going to be able to Charge more if you could make people More productive so what are the gains Like yeah we're really focused on making People more productive and also uh in All sorts of like side uh objectives of Making people more productive that we Didn't expect right like decreasing Error rate or 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 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 a dep 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 that's the Kind of stuff that we're that we're Really focused on if you were to pick a Number in the early tests of how what Percentage more efficient people were Would it be 10% a year 30% in the early Test so I think it depends on the on the Task and we'll probably be more we're Going to 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 um a workflow that might Take someone 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 been The whole time we only basically build Human a loop systems where you know what The models are doing 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 Be menial T ask that are 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 In revenue or $100 million in revenue For some big group you're going to be Able to do because the whole team can be 30% faster 50% faster at these Repetitive tests maybe you don't have to Add anybody so the company stays the Same size but 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 is really the focus all right Congrats to the team at LinkedIn they Just completed their march to 1 billion Users so what does that mean for me and You well we all know startup game is Rough now more than ever and you need Great team members to compete don't I Know it you need team members you can Depend on and there are so many great Employees out there ready to interview For your job and with a billion users LinkedIn jobs has the best candidate Pool out there hands down Bar None and You can land both active and passive job
Seekers the active ones you know about Hey they got laid off their startup shut Down they're actively looking for their Next adventure but what about those Passive job Seekers the ones who they Like their job maybe their boss is a Jerk maybe they're been there long Enough and it's time to move on those Are the passive job Seeker some of those Are the best in the world because They're highly sought afterafter and They're not actively looking but LinkedIn jobs will put your opportunity In front of those passive and active job Seekers so use LinkedIn jobs to find Your next amazing hire go post an open Roll on LinkedIn and you'll be 100% Certain that you have access to the most Qualified candidates available in the World in fact according to LinkedIn 86% Of small businesses get a qualified Candidate within 24 hours that's one day Or less and guess what first job listing It's on your boy jcal LinkedIn jobs Helps you find the most qualified Candidates you want to talk to and they Do it faster post your job for free at Linkedin.com twist it's linkedin.com Twi to post your first job free terms And conditions do Apply what will this look like in five Years if you're successful so this is The part that I'm I'm most excited about I think as we were talking about earlier
The 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 at handling higher Level things right maybe right now it's Like hey like here are the steps Required to 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 at open AI that was our Northstar are At Google uh when I was leading 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 ad Dept 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
Just how to read and write but the Consequences of doing things that a have A word is actually the critical path for Getting to generally intelligent systems And uh sort of the most the most Predictable way and so what I expect to See from the Adept product is that the Abstraction level what you can ask her To do will continue to get higher every Year absolutely fascinating and how far Away are we from AGI in your mind I turn On my computer in the morning I go to Work 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 Probably need some more information Based on what I've learned so I'm going To send them request for this Information and then schedule them uh For tomorrow and Wednesday is that okay Boom and it just like kind of tells me What I'm doing for the next three days When when will that happen had to 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 I'm not sure We'll get to recommendations being as Good as an MBA from a school and Somebody 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 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 this idea of a chief of staff Being able to watch and executive work And fill in all that connective tissue And advise them what you know where Their attention needs to be just done Completely by AI I think giving you a Couple couple of suggestions of which Maybe one out of three or two out of Three hits I think that's yeah sub five Years away yeah I mean a chief of staff Might give you five suggestions and you Say like okay we're going to go with These three and the this one I would Never do but thank you for the Suggestion here's a learning thing and This one you will consider it but let's Put it on the not right now list y Pretty amazing yeah I think it's a pace
Of progress you know I think right now The field is 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 mean doesn't mean the next three Years will be crazy capabilities 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 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 me I I've Been talking to 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 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 verticals can you actually carve An Niche I do like the answer to your Question the answer to the question of Silo 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 yep 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 yep and Pulled them together and I pulled them Together here's what you missed in Totality exactly they won't be Substitutes for each other they will Actually both coexist very happily and I Think the key with the agents thing is Just getting them to actually be Reliable and that's like I think that's The that's the key advantage that like That we're really trying to run at is uh Is you got to control the whole model Stack to do that all right everybody We'll see you next time on this week Startups Bye-bye
