How AI Is Changing Enterprise

wait a second if we could use AI to automate more we can build more if we could build more we can lower the cost of things if we can lower the cost of things then we can actually lift up anybody's lifestyle right now I think that we're in the middle of the Revolution and the revolution does not have to be uh Black Mirror it could be something that is driven by jeevan's Paradox driven by abundance for everyone and that's certainly the uh the timeline we want to be on so that's the future I'm betting on

[Music] welcome back to another episode of the light cone I'm Gary this is Jared Harge and Diana uh we're Partners at YC and collectively we funded companies worth hundreds of billions of dollars and today we have a really awesome guest Aon Levy than of box love that intro yeah Aon you're one of the best product CEOs out there public company as well what I write in my Wikipedia yeah yeah uh that's how I classify you we're in the middle of the AI Revolution so uh how are you feeling oh pretty good um it's a it's

a good time to be in uh in in software right now um so yeah feeling pretty good today something we've been speaking about for a while which I think we probably agree on is that the chat gbt rapper was like a bad Meme and that actually there's like lots of value and always has been in building apps on top of these Foundation model companies in fact the opposite might be true yeah which is going to be more value yeah I think so so it's interesting so um there's there's a like probably 2% truth in in

the meme and then 90% not not truth and so I mean PG you know with the sort of wedge theory is like actually you do want something that is is sort of a simple product that finds a little wedge and then you expand from there you know in the early days of of cloud if you were to be building software that let you manage documents and data you would have been like well that's a rapper on Amazon and and it was a total misunderstanding of the the entire scale software you have to build to make

the storage bucket be useful in in a particular application so on the on the rapper conversation the exact same thing is true which is how much software do you need around the workflow and the proprietary you know sort of business logic and the data that the customer brings that's actually the value not the not just like what is the the set of tokens that are coming out where it's a little bit true why why startups should be you know at least think a couple steps ahead is you probably don't want to be something that just

Chachi BT would incorporate so it's less that the model will incorporate your value proposition it's more if there is you know if the model provider also has a consumer scale application like you don't want to be right in the way of something that chat gbt will just fold in directly into its functionality so in that case I think you have to be you know sensitive to being kind of you know a quotequote rapper how do you separate out stuff that's going to get incorporated into the model from stuff that won't because I feel like the

hard part with that often is we don't know what the next models are going to be capable of and there's this General sense of oh well like anything could the Al inp into the next model if it's powerful and you know approaches some version of generalized intelligence yeah I mean I I look at all this through um just the B2B lens which I know that then probably you just lost half the podcast listeners B2B actually on the B2B front it to me it's a it's a it's a simpler question because um an Enterprise doesn't want

a model it wants an outcome uh it wants an outcome of customer support you know conversations being answered or um you know Healthcare um uh you know transcription going into an EHR system uh or an automated workflow of reading documents and contracts and plugging that into a a contract workflow so the model getting more intelligence is actually usually a better thing for anybody building software in those use cases because then you're doing Less in terms of of of you know hacking your way into the model because it's sort of insufficient at at solving that particular

problem but what the customer actually wants to buy is like I need software that will plug into you know my Erp system that will plug into my support system that will power the workflow that that lets the customer do a password reset like that's actually the what the customer wants to buy and what the model is doing is is sort of you know really abstracted from the ultimate customer value proposition so I think as long as you're You're Building uh you know software that that really can deliver that full outcome to the customer and and

you know two years ago the initial wave of these these use cases started to emerge and I think the the companies that will do best you know are ones that that realize that that you need to abstract the model away as much as possible from the ultimate value proposition and and then you just incorporate all the model updates as quickly as possible for your customer and again they just buy the outcome of customer support but it's just getting better and better every time there's a model Improvement so I wonder uh maybe one good analogy is

when you were building box for your customers they didn't care what was the underlying database or cloud or what was the networking gear or all the hard drives that were running it was all about the end user experience at the software level yeah and the analogy to today is the end users of B2B AI workflows don't care whatever model is or how it does it yeah but that It ultimately does the workflow yes yeah I think uh that that is absolutely the conclusion um you'll you'll often you know get some idiosyncrasies in different organizations where

they do care okay where you know where's your data center hosted or you know what what's the what's your infrastructure provider but that's a small minority in AI I think we're going to go through a temporary period where you do see differences in the models for anybody that that has you know a Discerning you know set of of of skills on this front so you know you can see this in like cursor you know with with anthropic right like people like that combination and they and they can sense the differences of of the output but

if you you know fast forward five years I I think you'll see a convergence of of basically models and intelligence to the point where you you wouldn't really distinguish the the quality levels that much for 90% of of business use cases it's definitely interesting to see how especially developers have developed different preferences for different models like I remember at our AI Retreat a few weeks ago um anthropic and Claude has also emerged as like the preferred llm to like orchestrate your agents like if you have multiple agents and you want sort of the llm to

intelligently call the right ones people seem to prefer Claude for that what do you think is going to happen to the model companies themselves in this world though probably everybody needs to update their understanding of what what a model company is just in general I actually think there's very few model companies there are sort of AI companies that have model you know Frontier Model Labs but but increasingly they're selling software to either consumers or businesses like I don't even know who I would consider to be a pure playay model company at this point you know

anthropic if you look at their their software Revenue um you know it's it's effectively an API business for for Enterprises I'm sure they have you know some some large scale consumer kind of CLA business but you're really paying for the security the compliance the governance the Privacy the uptime the slas talking to somebody that that you know manages your account and the the model just continues to sort of switch out underneath all that uh if you look at open AI Revenue anything that's been kind of leaked publicly it's very clearly a software company at this

point that has AI models that that power its underlying software Google obviously you know is just gcp um and then and then meta doesn't need to monetize it because they can just open source it so maybe xai is almost the closest thing to now a model company but that will show up in grock ETC so you know what you probably wouldn't want to do right now uh is is start a pure playay model company expecting you're going to have like licensing you know Revenue by just selling your model to to people to go and and

use if you don't have enough other kind of surrounding value proposition that that you know again lets you get incorporated into into Enterprises or has a large- scale consumer application where you have some degree of kind of uh traffic that that you know keeps people within your ecosystem um I think it' be very bad to be just a pure play model company uh in uh in at this moment just because you have you have enough different business models that have emerged now in AI where where it's going to be pretty hard if if your Pure

Play business model is just pure AI tokens because you know you always have meta that will will always create a counterbalance by just open sourcing you know a Frontier Model that that kind of wipes you out now deep seek and now deep seek and so and and and that's what's amazing is like now that like you can basically guarantee meta has to do anything deep seek does because it obviously has to stay in the game on the open source front and so we always have uh with there enough dynamism in this industry that that basically

ensures to to Gary's opening point that like the cost of intelligence is going to go to zero like it's just like absolutely guaranteed so extrapolating what does it mean for startups as intelligence becomes commodity basically well uh the good news is we kind of know uh we kind of know the Playbook on this uh with One X Factor which is like AGI and and like what is the ultimate you know kind of exactly a little bit of an xfactor of like wiping out all of all of relevant business models that we don't even have money

in the future but like if you if you put that to the side um I think these these companies need to look like software companies and and it is it it's sort of Back to Basics which is you know we used to have an API into a database we had an API into storage we had an API into compute now we have an API into intelligence that intelligence is it should be you know basically the cost of that intelligence will go down to the cost of the bare metal so whatever the underlying cost of the

GPU is that's what you're going to pay um you know with a little bit of margin from a hyperscaler but the the cost of the ACT tokens you know will converge at zero and so then it's all about you know do you build software that takes you know this complicated technology and delivers it to customers to solve real world problems and so you know you guys have talked a lot about vertical you know uh AI I think that's a massive play I think there's going to be uh certainly a whole layer of AI software that

that kind of stitches together different AI systems so you have horizontal plays you have vertical plays I think the idea of every single industry and every job function probably will have some degree of new startups and agents that that get built out for those uh those those like slots um I don't know if you guys have like a whiteboard of every industry in every job but like but like you can just basically play you know Bingo on that and then and then until it's fully filled in like there's probably still opportunity left with an AI

we figured out the first wave of SAS of of how to do this um uh you know YC was obviously a big part of of an a number of major kind of category killers in SAS and I think we'll see the same Playbook happen in AI one of the many interesting things about deep seek was specifically it's the first open source reasoning model like in the short term do you think there's like new ideas in the Enterprise in particular that are going to come out because now we have open source reasoning models so what we've

seen is um uh so we we we do a number of benchmarking kind exercise internally for for the reasoning models versus versus uh you know kind of let's say non- reasoning models and some things that're they're actually better at some things are weirdly worse at um uh and uh I I don't think we've even discovered why they're they're worse at these things um maybe they overthink a problem in general I would I would kind of just argue that anything that directionally improves intelligence you will see B2B use cases you'll see the value of those use

cases go up because um you know you can begin to reasonably chain together more more agents working together you can get more agentic workflows happening like anytime we can get the intelligence factor to go up um I can now reliably introduce this for a more important business process and so you know in the Enterprise you can almost think about it as there's some probably you know either 2 by two or chart I don't know if anybody's made it but like kind of like mission criticality of the workflow AI level of intelligence and and and you

know kind of there's an element of like you can't introduce it to you know Clos banking you know that like you know a banking systems you know sort of you know end of day data you know yet because it's it's not particularly deterministic it's it's you know we don't sort of you know know all the the answers that it's going to give but it could write a summary for for you know a new product launch at a bank um uh or it could help answer you know banking uh you know uh product questions if you're

a consumer so there's a Continuum there and as as we get every degree of intelligence going up we get more use cases that we can now implement this for um and then there's another axis which is like how many of those use cases you know can you string together to to complete like the full the full workflow of that business process and that's yet another I think access that that that uh were early in but like you know I was um I was in New York a couple weeks ago meeting lots of Banks and and

you know just generally what you think of like the New York Industries in in Enterprise and I would say we're like 10% of the way into the adoption of let's say just like General chat so like assistance and like 1% of the way into adoption of anything we would all call agents like and that's maybe even inflated numbers when you're in the room with like the banks the 14 500s or the people making their decisions in Enterprise um do they really have zero interest in the underlying models like is deep seat coming out just like

a total nothing Burger for them and they just care about what you're pitching them and offering them or do they do they have interest in the actual underlying tech there are people like us and the people listening uh at every company in the planet and so those people care uh by the time you get to the let's say line of business so I'm the head of wealth management at a bank they don't care so but the CTO cares and the head of AI cares and the and the you know the the it folks that that

dabble and they're hanging out on Hacker News like those people they they care because they're they're using cursor and they're using they're seeing the differences between between between anthropic and and open AI you know tokens within within that but when it goes to to talking to you know an executive in the business or the daily end user they they have no interest uh it's all it it's all you know a foreign language to them and I think that will remain the the the you know the way forever I I think more of the expectation is

is that again these things will converge and what's amazing about AI is because of the I don't want to call these the models fungible but but directionally fungible because they're somewhat fungible you will see characteristics that we've seen in in other areas of compute which is any uh best-in-class model eventually has to be you know match the price of anybody that beats them in pricing because because you can just switch to a slightly inferior model even if it's like inferior by 1% the risk is that you could switch to that and be and find it

acceptable for for you know 80% of your use cases which then by definition means whoever is at the frontier actually still has to match the pricing of somebody just slightly worse than them um because they could just you know the US actually doesn't care do care and their business could evaporate if if they don't do that which means ironically you could actually stay on one of the providers you could just pick a provider and you kind of know that your your tokens will become as cheap as the second or third you know cheapest option because

that that first you know whatever that first provider is the marginal the next marginal customer doesn't have to choose them they could choose the second or third player so you which eventually then you know you you you run that experiment out you know 10 years you converge on basically the same pricing which is what we've seen which is like the difference in pricing of of you know uh storage buckets you know between the top three or four hyperscalers are are not so different to drive business model you know fundamentally different business models um in the

software stack similar to compute Etc so really you're making a choice based on you know some other set of reasons like how much data do I have in the system what are my workflows that I've built in the system um and uh and then I think the again the price of the AI eventually becomes largely the same actually I think what you're saying applies to to what we're seeing for startups I've done a number of office hours with AI startups that are selling to Enterprises and a particular story is this company that scaled to 12

million Revenue within a year yeah they actually switch models underneath a number of times and the end customers which were these big Enterprises didn't care yeah what they cared was that ultimately the contract and expectations was that just get the workflow done with this level of accuracy done yeah and as the cost per token has been cheaper they actually have been increasing their margins I think when they started launching I think their margins were around like 30% yeah the next cycle of iterations middle of last year with all the model releases got to 60% and

I think now they're at like 80% that sounds like fou storage yeah exactly we love that so that's kind happen I like conrete example for this company that done office hours and exactly I mean your analogy is actually quite quite literal so we so right now publicly we have 81% gross margin um um if you had said you know let's say when we started the company in in 05 that a a business that was perceived to be storing data would have 80% margin you'd be like no that doesn't make any sense because because like people

are just paying for the storage and it's like no we have I mean you know we have nearly a thousand Engineers of box one to two% of them are working on what we would call file storage so what is everybody else doing we're building software that that is the abstraction layer of of compute and storage and databases to produce workflow and data governance and Automation and insights on data so the storage is is is now you know a small fraction of what we overall provide so similar to tokens as as you know the tokens go

down as a ratio of what you're really delivering as value is that software stack so I think you know probably one way to think about it as a heris is like how much software is necessary on top of the output of the tokens for your value proposition to work you know successfully for the customer the less software there is probably the higher risk you'd have to either more competition or commoditization the more software there is where the tokens are just one you know kind of contained component of the full thing then you're probably you know

in a position where you can you know you can build a moat you can get stickier you can then you know solve more of of that customer problem but you might get to the point where where the customer pays for for a discrete outcome this is like one of the big open questions I'm sure you're seeing it in the batches but like what is your pricing model do you do you pay you know let's say you're a startup that does AI lead generation do you pay per lead right that's that's a a fairly obvious you

know kind of you know thing that that you'd expect and then and then basically that company now could be you know or or do you even pay for like qualified lead that the customer sort of says is is actually successful so so like there's a whole Continuum of like I pay for the a successful outcome I pay for any outcome or I pay for the underlying kind of resource utilization which we also see in like coding startups you know where it's like okay I want to buy some unit of compute measurement that goes into useful

work um but the cool thing is we're going to see you know mix of of all new business models and software that we haven't seen before this would been one of the biggest changes in the advice I give to the startups during the batch is I like it was really hardwired into me when a come when a startup comes to we like we're getting like a a pilot or they're going to pay us as we go or something like that to like say that's like not a real customer like you have to go back and

you have to get them to sign like an annual contract and like lock in Revenue otherwise you're just kind of wasting your time with someone who's sort of one foot in one foot out but over the last year in particular it's when I look at the most successful companies essentially often they're like replacing a BPO or some sort of service like that the customer actually wants usage based um and the like the revenues just keep going up and up and up so I'm no longer like oh you have to sign an annual contract yeah yeah

I mean we you know for a lot of areas where we're sort of you know there's a a direct relationship between the the thing that the company is selling and then you let's say labor on the other end you do need you know a lot you know like you need you need long-term contracts because you have to hire people you there's a lot of infrastructure you have to build out the great thing about AI is it's entirely elastic yeah so it it that's I mean we we we're going to have to you know imagine new

world where all of a sudden I have elastic resources for things that otherwise used to be very operationally intensive so one day I can just say hey I want to I want 10,000 leads generated go run you know AI to go do that and in the in you know in a traditional way of doing that that might take months of hiring and Staffing and building out teams now it's you know a week later you're you're Off to the Races you're generating those leads you know you can kind of go through any analogy of the business

um and uh and that becomes possible so totally different relationship between uh you know the company and getting getting out outcomes and output totally different relationship also than for the software provider and what their what their business model is with that customer eron can we go back to your trip to New York City yes just any of my any of my trips well like you spend a lot of time talking to senior execs at Fortune 500 companies about their technology and AI strategy probably more than almost anyone in the world and I I was really

curious what those people are thinking about AI are they focused on it what do they think it's going to mean for their business are they building AI initiatives internally are they trying to buy products from other people what's happening yeah I mean definitely all the above did you see this this thing that went viral like two weeks ago David Solon U CEO of Goldman Sachs S1 yeah the S1 prep he basically had this quote at a at an AI event at Cisco they're doing projects internally where where AI is writing an S1 in like 10

minutes or something and it used to be a team of of six people that work on that Etc the exact same quote uh just parallel universe quote 15 years ago let's say in the early days of cloud just as a as a useful kind of comparison I'll probably keep coming back to the cloud thing it probably would have been a banking CEO saying we'll never go to the cloud like we don't trust the cloud we we don't and and now the exact opposite which happened right didn't Jamie Diamond Jam Diamond did I think he's he

sort of evolved his thinking but you had that kind of across the board and these were these famous moments as like we'll never be a cloud company you know we don't trust it we don't want to move I mean Amazon it's a bookstore like like that was the that was the refrain and it made sense I mean I I even said that when I saw S3 like the bookstore is going to power like what so think about how different of a world it is that the CEO of Goldman Sachs is basically saying like this is

now what's possible he wasn't saying that in like a we shouldn't do it way he was saying that in like a we need to open our our eyes uh you know up to all of the the potential use cases that AI is going to have in the business and he was saying it as a way that they're leaning in and starting to try out all these use cases so for that to happen for a top five Bank in the world at this early in the cycle you know it only goes kind of it only goes

more aggressive from there because he's in the most regulated of all all the businesses in the in the most important Financial Market in the world and he's already leaning in is that because he's like a particular early adopter or are you just seeing this across the board where yeah I mean also he's like a DJ so so so maybe yeah I maybe he's hanging out with like EDM people that are just like really into AI music okay so you know 10 years ago we'd host these dinners 15 you know cios from different Industries heavily Financial

Services let's say if it's New York and and it's like the the you know we are going to try Cloud for this one tiny part of our business we don't really think we can scale the idea of being Cloud first is like it would be like totally in you know an anomaly like you would never like a bank would never say they're Cloud first 10 or 15 years ago uh today it is it is sort of like we're trying this in as many areas as possible um everybody's still insanely early because you've got privacy councils

compliance councils regulatory bodies that have to look at everything but everybody understands how how big of a Title Wave this is going to be in their business um on a few Dimensions one they know that the workforce is going to completely change they I think there's a recognition this kind of Hit Me Maybe about a year ago in some of these conversations there's a recognition that that that basically if you're entering the workforce today you've had now a couple years of chatu BT of of like college like you don't like they native it's an AI

native you know era of of the workforce and you know we could make some jokes about it maybe two years ago like oh my God the writing essay is I can't believe it but like I I basically almost don't search the internet anymore like I only know how to use AI to find information and guess what like I'm I I find 10 times more more information as a result of that so so actually in many respects the AI native you know people will be Smarter on the topics that they decide to go in on um

than than than the prior generation of of whatever that is so what does that mean like these Fortune 500 companies are changing how they hire or so I don't necessarily know how they hire but but it'll become clear that if you don't have ai uh if you're not an AI first bank or media companyi strategy literally what is your AI strategy because because why would to the customers or internally the the the Enterprise will will basically realize that they can't actually hire the Next Generation you can't go from all of a sudden I have this

AI native way of of operating and college or high school to going to a company that makes me use you know the equivalent of a fax machine you know level of Technology it's just like they won't be able to hire people and then their their competition will have more output they'll they'll they'll do more Investment Banking deals they'll onboard customers faster they'll get better Financial advice to their clients than the company that that that that sort of doesn't do that and so everybody's sort of realizing this is actually a competitive issue Cloud didn't really have

that cloud was an purely an efficiency story it was like it was like ah yeah I don't really want to have to be building data centers as much elastic capacity sounds pretty good I want to be able to test this new product faster it was not like my customer is going to experience a different thing about my about the output of my company whether I'm cloud or not Cloud now I think we all believe that that was that that that there is a difference there it was not tangible to the buying side that cloud was

going to make your company five times more competitive in a way that AI is is very clearly I I think resonating that actually your company's competition will now be at risk you're not a native because like with Cloud early on a lot of the benefits were actually to the startups who pushed it Forward because they just wanted an easy way to like get set up and not have to deal with hosting but you're saying it might be the opposite where with AI productivity tools startups don't really need actually maybe the the Counterpoint to me what

this sounds like is actually way more opportunities for startups it's just all the office hours that we're doing I think it's the fastest I've seen B2B SAS AI companies get Enterprise deals and I think you painted a very good picture what's the Vive shift yeah if if I can just flip in one funny anecdote uh the I I went to this banking conference eight or nine years ago and I did this little keynote uh at this banking conference and it was all about like we have to be Cloud first and and Enterprises have to modernize

how how they operate with the cloud and I think I've never been I think I've never bored an audience more than than that keyot like I remember getting off the stage and nobody caring like it was just like cuz it was like why are you talking about backend infrastructure like like nobody cares like yeah whatever we could be in the cloud we could not be in the cloud you know at our levels of budget if we're spending like a $500 million or a billion dollars a year on it who cares if if we save a

100 million because some of it is elastic or not like it's like not that big of a deal if if you were to do the same thing but an AI first to non- aai First Enterprise people would be like oh like actually I probably can't run my business anymore not AI first because you would just show people like like do you know the productivity gains of somebody using let's say cursor it's like you will be blown out of the water competitively if you do not know how to build an AI first company right now and

yeah has box invested in any internal AI tools to speed up how you run the company yeah so uh a few categories so one uh we're we we've been rolling out AI for for on the coding side um and we we're we're trying everything uh like internal tools for your engineers to Yeah Yeah so basically just just how do we make the engineering kind of more productive and that that's sort obviously what one of the biggest X factors of our business is can we output more code that is obviously useful and and and aligned to

our product road map so um we will be you know fully AI first in terms of how we develop um you know this year it's uh it's the it's third of The Big Year for all the change management on that we are you know incrementally rolling out AI for different customer facing things just again can we solve the customer ticket problem can we can we improve the the rate of response and then as an AI provider a lot of the Knowledge Management use cases we we sort of now do ourselves with AI so you know

if an employee has a has an HR question or benefits question we have a a feature that lets you talk to all your HR data um and all the internal Knowledge Management and so what what became this breakthrough for us was all of a sudden all the things that were inside your documents before become useful for now just interrogating with questions as opposed to reading documents and so there's a lot of just embedded productivity that that we focus on from that that standpoint what things do you think they're going to do internally and what things

do you think they're going to buy solutions for I I would basically guess that that again it kind of looks pretty similar to two kind of historical ways of of thinking about this um I think Jeff Moore created this but uh and if I'm getting it wrong again please Jeff Moore like like pop into the com the right name um and the idea was was sort of context is all this stuff that is sort of like you have to do it's necessary but it's not going to like it's not going to make your business uh

better than your competitor uh and so that's your HR System that's your Erp system like you have to have it it's important it has to be done extremely well but like your version of the HR System you know is not going to be radically different than than Bo checking any box you have to check the box but it's got to be a good box and and and and whatnot and then there's core which is this is like literally your value proposition like like you sell you know wealth management to you you sell Wealth Management Services

to to people and um and that that is something you you own if your thing looks exactly like your competitors then you have no there's no sort of you know profit you know margin that that you'd be able to you know you wouldn't be able to get reasonable profits you have to have something that's unique and I I think companies need to really understand which categor is which partly because if you get the if you put the core in the context then you'll probably be at a long-term disadvantage and if you put the context in

the core then you're wasting a tremendous amount of time and energy and and this is why I I like I I really enjoy that the the the Clara announcements or whatever it's like fun to read but I also think it's it's sort of misunderstanding the context versus core core thing like you don't need to build your own HR System I'm glad they're doing it I think we it's provocative like it's fun to see different approaches gets people going it gets people going you know gets our like we're like our juices are flowing but like the

average bank is just like not it's like not a priority for them to like reinvent their HR System now so so so I think I think whatever whatever is that for every industry you know in life sciences you probably really want to understand like how are you doing drug development and you should probably have a a a very strong AI team working on that problem because that's something that sounds like it's going to be IP for you but the automation of the clinical trial process that's probably context for you because everybody's going to want to

be doing that as quickly as possible it probably doesn't involve a tremendous amount of proprietary data and then and then obviously your CRM system your HR System and so on so it actually sounds like they're going to buy a lot of things externally because most functions in a business are actually contact yes yeah I think most most of the way AI will show up to a knowledge worker in 2030 will be from a what we would have thought of as an isv 10 years prior what's an isv for the people watching yeah it's just basically

a software you know provider there CRM system will still come from Salesforce or or or ex competitor uh it won't be that they built a homegrown AI generated you know uh you know CRM system they might talk to that CRM system through also a new vendor or something that they build internally it's interesting there's actually a lot of chat pods being uh built internally by companies right now I'm sensitive that I think it might be a temporary phenomenon um and I like ux better than chat but yeah yeah okay yeah I think we're going to

we're going to see a hybrid of of these two things um I I think the gooey is not as dead as as as people think you do see a lot of kind of chat interfaces where you're like you're like I think you just did way more work than just going to the dashboard like like I'm I'm 90% sure you just probably took all the savings from AI gains and efficiency and and and then just spent it on figuring out a prompt um that like a dashboard would have solved so but I think we'll have a

universe of both those things but I do think ultimately most will come from software as like a you know on a percentage of like like time that a a a knowledge worker spends inside of Technology but some of the most valuable things absolutely will be will be homegrown the algorithm you use for discovering the thing or personalizing the medicine or personalizing the wealth you know data or um you know Netflix's recommendation engine like those will be homegrown things maybe still using a a model from from a proprietary player but like the scaffolding there will be

I think a large largely built by buil built internally interesting so I guess the mental model for people watching might be that there's inside the house and outside the house outside is context which are just check boxes you have to check and those might be you know end to end things that just do the thing like Salesforce yeah I think I think that's right and I think the maybe another way to reverse engineering is if you were the customer of this company would you care would you care what technology they used for for that category

of thing or is it like just get get the box checked and I'm good yeah as a customer of Netflix I literally don't care what their Erp system is and then on the inside like uh the things that are core that you really need to do maybe the opportunity there is uh infra and you know Dev tools that allow an internal it team or internal engineering or AI team to run really fast but then create exactly what that business needs yeah yeah yeah exactly and then there's still there's a little squishiness in there just be

very littleit about it because like you also probably don't want to do homegrown Dev tools like so so you still might procure those and then there's this layer between the dev tool and the customer experience which is what am I doing with the output of these tokens that's something I need proprietary software built on like and and so what is in that universe but I think that like like for instance I think there's going to be amazing you know amazing Dev tool opportunities coming from from startups right now because it's a whole new stack that

you have to build out for for managing AI within your your Enterprise that in between B is actually we see open source a lot right like that's have you seen any renewed interest or any Trends on open source interest in the Enterprise I selfishly I love open source because we don't have to pay for you know what we're using um we should give grants well well it's actually I've think you don't pay for the hosted versions all the time exactly sometimes we do but but I was actually thinking about this I think I think a

a a thing that would be cool to see tech people do more often um is is just like start large scale open source projects and fund them I think more open source software for the common good of of just like lots of different Services I think is a huge net positive I'd love to see more uh I'd love to see more open source in general uh right now it's such a great symbiotic you know model because because you know the hacker Community or the startup Community they want to move fast they don't want they can't

they can't often afford the licensing of whatever that thing is the large Enterprise Community you know they want support they want to have people that are experts in managing this stuff and so it's this great relationship which is like you have this great good for for society and you still actually can be building a successful commercial business because lots of people want to pay for for the commercial version of that yeah Aon in the in the early days of this cycle there was a lot of concerns from Enterprise about the security implications of using hosted

models like a whole bunch of companies like ban chat gbt use internally what what's happening have they gotten comfortable with the idea of having all of their data go to open Ai and anthropic or did they still are they s really worried about that and trying to host open source bottles and things like that on Prem yeah I I think you'll see a different different categories uh by industry so a lot of times they'll go into you know a bank and they'll say um and they'll be proud of this and and as they maybe should

be they they'll say you know we have our own kind of Enclave version of of xod model and then we built out a wrapper on top of that you know for for deploying it to employees um and I think that that you'll always have some percentage of the market do that you know 10% of of the market because you you still have a lot of people that have on- Prem systems for a similar reason that will always be there um but the Comfort level is absolutely increasing that tends to always happen in if if the

industry or the the company takes it very seriously so opening eye has taken security privacy compliance you know regulatory controls very seriously and so then that builds trust over time so then more people can um Can can go uh can can experience those um you know those use cases so anytime a software category matures and and becomes more Enterprise grade or military grade you know you'll see a COR correlation of the the amount of trust and in putting data into those systems and I think AI is no different than then again software and kind of

cloud in that respect maybe part of it is just a lot of the behavior is from all these Enterprises they gotten a lot more comfortable because of what you paid with Cloud right they kind of already have preference on how to do this hosted and okay we know how this looks so it's a lot harder for you to sell on that first cycle this cycle you kind of compounded it for this next generation of Founders so thank you people need to be really thanking me um I am not actually you know it's funny that you

put it that way I don't think I've ever been thanked for for the hard work consider yourself thanks the years of of dinners all the cios that made it possible for your AI startup to get sold in the Enterprise I'd like a big thank you I have not been respected that is not that is not shown in in a lot of ways but I appreciate it every single white hair is a dinner that you had to do to teach people that you should buy this it's uh uh this is the only way to get that

um I would like a plaque at the office I I don't know why I don't have that yet CU like none of you have gray hair so like this is this is I'm I'm responsible for for AI working now um yeah it's but it's an interesting question right because um and this is this is what's so fun about about the compounding nature of Technology AI could not have happened in 2005 let's just pretend the breakthroughs actually happened let's let's pretend an alternative Universe okay where where the exact same somehow like Jensen had made gpus as

powerful as they were now in ' 05 but everything else was the same okay weird world but let's just say okay and you're imagine going to a company and you're like okay we know that all of your infrastructure is on Prem and we know that all of your software is on Prem and we know you're on seow and we know you're on on people soft and we know that all of your data is in this system but if you just move it to this other thing that you have no control over it's in a data

center you don't know about you know you have no no ability to kind of go in and look at the data center and and look at the the the the hard drives if you just move everything over to this think about all the intelligence I mean it would never it would be DOA like this industry would just be over it wouldn't matter what the tokens are coming out with so you actually it was a requirement as a preck to have Cloud have happened to have sasive happened and then now ai happens building on that um

and then and then even like even the consumerization piece is is so interesting um imagine a world where where we were only an Enterprise using technology we didn't have the kind of consumer access to technology that that eventually happened with mobile Etc you wouldn't have people seeing Chacha and perplexity and Gro Etc in their personal lives and then saying wait a second why are my it systems feel so archaic by by you know comparison that's what's causing all of this pull also so you needed you needed consumer adoption of technology and then and then the

fact that now a billion people billions of people can use AI to then pull that into the Enterprises you need the Enterprises infrastructure to be modern enough so it's it's this incredible Cambrian explosion of now opportunity because we're in a moment where enough of the the this the kind of core Plumbing has been built out also Enterprises just became used to using like different software Solutions versus just relying on like the exactly yeah 100% like they like they now there's their now some of them don't like this but their it stack is now hundreds of

vendors versus a dozen um and it's a and so they can accept okay I'll take a with anthropic like this is totally it's it's you know it's only now 1% in increase in my total kind of it um you know vendor Universe versus you know 15 years ago be like N I can't I can't bring in something that's not Oracle yeah well keep accelerating uh please just keep keep driving the startups that that causes to happen you had this front row seat to the transition from on-prem to cloud and now we the dawn of the

next transition from Cloud to AI how do you think it's going to play out similarly is going to play out differently and and how do you think that relates to The Tam for software going forward yeah so uh if I could if I could try and merge these actually there's there's a there's a cool connection point so the probably single biggest bare uh reason why people didn't invest in SAS in the mid 2000s was was they they they thought the market sizes would be basically the same size as the on-prem software company and and so

if it was the same size as the on-prem software company but also the the software company that's already there is the incumbent you know it's like how do you squeeze out enough enough money to to kind of make the business really really you know make make it really interesting and what everybody basically got wrong was it turned out that the Tams were probably about 10 times larger and why did the Tam grow so much because um just to like bore everybody if you wanted to buy a CRM system in two in 20 sorry in 1999

you had to be like okay I'm going to go to the systems integrator I'm going to get a data center I'm going to buy a bunch of servers from people I'm going to install some software I have to manage the network of that and like you know lo and behold two years later you might have a CRM system and you probably spent $510 million on the full project to do that so think about who is the market that then can Implement a best-in-class CRM system it's the world's largest Enterprises you know 5,000 companies 10,000 companies

Salesforce comes out and they're like for three seats online with your credit card you have a CRM system as good as seble obviously there'll be some Nuance because didn't have as much functionality but like for that company that was as powerful as seil you know getting started now all of a sudden your Tam is basically every business on the planet so you go from a a market that had maybe 10,000 customers 20,000 addressable customers to now a market that has 5 million 10 million potential customers it is a totally different scale like you know two

three orders of magnitude more scale that you can now go and serve we we had a similar experience which was you know the industry we were disrupting was like Legacy Enterprise document management Enterprise content Management Systems same exact thing as seil in terms of like we would read the S1 of of our biggest incumbent competitor and they were talking about like a thousand customers or a couple thousand customers and and literally you know now we have 115,000 but like but like but at the time we had we had I don't know five or 10,000 when

we started thinking about disrupting This and like the scale was just completely different so so that meant the market sizes were so much larger um you know service now today uh I don't know the the exact latest market cap maybe it's $150 $175 billion their incumbent competitor when they when they were first growing and disrupting the market today is worth maybe about 5 or 10 billion so so if you had looked at this company 20 years ago you'd be like at best service now should be a five or1 billion doll company if it just was

like a better version than the current thing and it turns out it's it's 15 times larger than than than what you would have thought Salesforce did the exact same thing in so on AI I think has a similar dynamic because you're basically increasing the total spend on software so it's not so much that a new set of companies will buy software the first for the first time it'll be all companies use software to do things that software didn't do before and that will take from budget that that previously was sort of untapped you know from

software so um the budget will be from a variety of things um uh but uh often because now the software is doing useful work for you you can now afford to spend even more on that software because the alternative was a much more expensive sort of proposition here's where I think people kind of get wrong though they they think about it as Zero Sum from well then then all you can do is sort of take from the labor side of that spend but it actually just turns out most companies aren't even spending on the labor

side they're just not doing the thing so so you know most companies are like just like globally are not like spending time to translate their advertising into a different language so it's not that that oh the the the market for the translation services are this big and we're just going to digitize it it's like no a 100 times more people will do translation um the you know in in our business like we have you know software now that reads your contract and pulls out the critical data from that so you can automate a contract workflow

and like the number of people globally that are reviewing contracts and pulling out that data maybe it's you know 10,000 50,000 people I don't know the exact number but a very small percentage of companies are doing that with their contracts so they will now decide to prioritize automating a thing that they didn't automate before you know cursor the you know the as just a you know back to that example or repet or Devon or whatever there's probably not a single dollar that's being spent on that technology that comes from from takes away from what people

are currently doing it's purely additive because now it's expanding the use cases that that software can can can kind of tie into so I think we're in for a potential scenario where the the size of software now you have to include AI in that could be five times larger uh in in the next decade because it just it IT Supplies the actual underlying work that you actually bought the software for in the first place um and that just changes everything because now we're we're you're going to be paying pay for work as a paying for

for a tool that enables other people to do work I think that's such a powerful AI white pill actually it's not merely you know Zero Sum we're converting payroll into software revenue and haha that's it it's actually we're going to do things that enterprises should be doing would have been doing never got around and then you know actually the people who are the consumers on the other end they're going to have better products better services like the thing will actually just be better for I haven't yet read like the full like economic study on this

but where economists always get this stuff wrong is you know they do probably by default tend to have a kind of a zero I mean you wouldn't have like you know javon's Paradox if if economists always knew you know how to anticipate these things but like what I think they often get wrong is they look at the total amount of Market labor in a category and they're like well shoot if AI automates that that's now gone look how many jobs that is you know I I think we should debate it we should talk about it

because it's a very you know serious thing but what they don't actually ever think about is the micro the the more microeconomic impact of this so if I'm a company and uh it could be box or or it could be a 20% company and I use AI to let's say code faster okay well why am I coding faster because I'm GNA I Want to Build a Better product for my customers if I'm building a better product for my customers my Revenue should be growing faster if my revenue is growing faster I probably then am hiring

people to go and do things to drive that Revenue growth maybe it's people selling the software maybe it's customer support maybe it's HR people to help scale the operation and eventually I'll get to a point where I say should I reuse those dollars that helped me grow faster to hire more Engineers to grow even faster and to build more of that road map and and if if we were in a a market that wasn't competitive maybe you could say you know what I actually just want to take the profit and be happy but we're in

a competitive market so if you're the one company that decides to sit on the profits that AI generated and just and just live off of higher profit margin 20% 30% 40% profit margin as a company you'll just have somebody come into the space and say no actually we'll just I'm fine to have 20% profits and do that same thing and then that company will will eat into your lunch so you actually then reinvest those dollars back into the things that are helping you grow faster and that's actually like the microeconomic outcome of of automation is

is you decide that you take that efficiency gain and you redeploy it into the business in something that will make you more competitive or grow faster or better serve your customers because you're in a competitive ecosystem and that's why I think as you know over time you'll yes you'll have some displacement in different categories but over time this is why It generally just looks like an upgrade to just how we tend to work you know in the world like we just tend to use tools to to work faster to make better decisions to build better

customer products the customer gets a better result out of that but we reinvest those dollars back into the businesses because we're in competitive ecosystems and then the ultimate winner is the consumer consumer always wins on this stuff right right now with One X Factor just to like bring it home to SF like like assuming we have a regular regulatory environment where those winnings can actually turn into Surplus right so so if those if it turns out that that we take those winnings and then regulate you know the ability to build housing then all of a

sudden everything's still just as expensive but like like the the total utopian which is sort of the abundance thing is like wait a second if we could use AI to automate more we can build more if we could build more we can lower the cost of things if we can lower the cost of things then we can actually lift up anybody anybody's lifestyle right now you know the the the the 10-year-old in an underserved Community is now all of a sudden has access to the world's intell in the form of an AI agent so they

can they they now you know are able to be educated better uh if we can lower the cost of delivering services to people then you get you know better healthare that's like the ultimate utopian state is we use this automation to to actually deliver better outcomes for for the world um and that will require tons of jobs uh as a result of that yep we can be a society again yeah because of AI there you go Aaron thank you so much thank you thank you so much for being with us great I think that that's

a great place to end just because you know to be continued like you know I I think that we're in the middle of the Revolution and the revolution does not have to be uh Black Mirror it could be something that is driven by jeevan's Paradox driven by abundance for everyone and that's certainly the uh the timeline we want to be on so let's do it that's the future I'm betting on [Music]