Software in the Age of Agents | The a16z Show

Seema Amble, Steven Sinofsky, and Elena Burger unpack one of the biggest questions facing enterprise software: what happens when AI agents become the primary users of software instead of humans? The conversation explores the rise of "headless" software, why APIs and agentic workflows are reshaping enterprise applications, and whether traditional SaaS products are becoming systems of record rather than systems of engagement. They discuss Salesforce's Headless 360 announcement, MCP, enterprise software architecture, and why AI may fundamentally change how businesses interact with their data. Along the way, they examine what actually makes enterprise software sticky, why replacing systems like SAP and Salesforce is harder than it appears, and where startups have the greatest opportunity as AI reshapes the software stack. Timestamps: 00:00 - Intro 01:00 - What "Headless Software" Actually Means 06:57 - Agents, APIs & the Definitional Hell We're In 10:00 - What Makes Enterprise Software Sticky 15:00 - The Death of Software? Why SAP Isn't Going Anywhere 22:00 - Vibe Coding Your Way Into Enterprise: Why It Fails 29:00 - Exception Handling Is the Entire Game 37:00 - Productivity Creates New Scenarios, Not Fewer Jobs 54:00 - Where the Biggest Startup Opportunities Are Now Resources: Follow Seema Amble on X: https://x.com/seema_amble Follow Steven Sinofsky on X: https://x.com/stevesi Follow Elena Burger on X: https://x.com/VirtualElena Related Reading Is Software Losing Its Head? https://a16z.com/is-software-losing-its-head/ The Death of Software? Nah. https://a16z.com/death-of-software-nah/ Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Seema Amble?

  There are many things that made software sticky, but a lot of it had to do with the fact that it was built around like the way a human interacts. In an agentic world, do you actually need that? The data, the logic, everything stored below it is really where the value is.

• 0:15Steven Sinofsky?

  There's this wild underestimation about like you could vibe code your way into enterprise software. Larry Ellison at Oracle, he went on a rant about how enterprise software was so stupid because everybody customized it. The minute you automate the most mundane thing and think you have it all squared away, whole new things appear.

• 0:34Seema Amble?

  Misconception right now is that you can just have, you know, Postgres database and APIs and then bam, like you can replace SAP. And that's like absolutely not true. That piece around the logic and everything else that is in captured in SAP is way, way more important than the fact that like, oh, this data just happens to be in this database.

• 0:54Steven Sinofsky?

  One of the things that happens in technology shifts is nobody understands exponential when it's happening. The biggest opportunity right now is-

• 1:02Erik Torenberg?

  For decades, enterprise software has been built around one assumption. Humans are the primary users. But what happens when AI agents become the ones reading data, updating records and completing workflows? That shift raises a much bigger question than whether software gets a new interface. It challenges how enterprise software is built, where value lives, and what makes platforms like Salesforce and SAP so difficult to replace. In this episode, Seema Amble, Steven Sinofsky, and Elena Berger discuss headless software, AI agents, enterprise architecture, and why the next generation of software may look very different from the SaaS products we've used for the last 20 years.

• 1:45Speaker 03

  Welcome to the A16Z podcast. I'm here with Seema Amble, a partner here on the enterprise team, and Steven Sinofsky, who is a board partner at A16Z as well as a former member of Microsoft, a friend of the firm. And here we are today to talk about a piece that Seema wrote about a month ago called, Is Software Losing Its Head? And I'll let Seema talk about it in her own words. But this piece was written a couple of months ago. Salesforce announced that they would be going headless. And today, we're here to kind of discuss what does that mean?

• 2:26Speaker 03

  What does that mean for the future of SaaS products, the future of kind of software more generally? So, Seema, can you just walk me through first what headless software means and explain kind of what changes it introduces?

• 2:40Seema Amble?

  Yeah, so headless software is not a new term, but I think has really risen in the public domain of interest in a topic that people are talking about. One of the interesting news points has been Salesforce making this announcement. They were launching Headless 360, which was really, in classic Salesforce motion history, a marketing announcement more than anything else. But it does capture, it's an acknowledgement of what's happening, which is traditional software had been built around humans accessing it. And it was workflow to capture data, and we could talk more about what that meant. In an agentic world, do you actually need that? The UI doesn't matter because the agent isn't accessing the software via the UI. We could unpack whether the UI matters or not, but in the idea of it being headless

• 3:23Seema Amble?

  is the data, the logic, everything stored below it is really where the value is, not just the workflow software that's being tracked at the top.

• 3:32Speaker 03

  And this was announced a couple of months ago. I'm curious, in the past couple of months, what have we seen? I mean, you wrote, even in the piece, kind of, at the beginning, you were a little bit funny, maybe, and you said, is this really even that big of an announcement? Is this sort of a rebrand of APIs that they had kind of already made available? So, does this feel like a significant change? Is this maybe more of a branding exercise? And kind of like, what have we even seen in the past couple of months since we've been able to kind of observe what changes have really happened?

• 4:04Seema Amble?

  So, I'll separate it out into the Salesforce context and then the broader context, which I think the broader context is more interesting. In the Salesforce context, probably not that interesting. I think Salesforce, I think rightfully, again, is acknowledging a shift that's happening in the market. From what I could tell, nothing actually changed. Their 360 product was same APIs that had always been exposed, now rebranded as their 360 product. And APIs have always existed. I shouldn't say always, but for a long time have existed. But I think the broader trend here is, you know, Salesforce, among others, are thinking about how they build themselves for the agentic world. And so, if an agent needs to access the data in a CRM like Salesforce, what are they doing? Are they doing it via the UI or the API?

• 4:48Seema Amble?

  And that's the Salesforce is saying, okay, hey, we know what is changing, that there are agents who are needing to access the data. Let's offer a headless version for them to interact with the data versus going via the UI. That said, again, I don't think any actually changed in the Salesforce context, but Salesforce is the old one. Another example is Notion has a headless product. And actually, I think that makes even more sense because it's much harder. Like, I think many users of Salesforce are probably less technically adept, less likely to be building their own agents, although there are many, many more people who are doing that with Salesforce. Notion users, all things being equal, are more, I'd say, tech savvy and more agentic as builders. And I think Notion is one of many other companies that is also trying to figure out, okay,

• 5:29Seema Amble?

  what is it that I offer and what APIs do I expose? And Stephen will talk more about MCP. And again, I think a lot of this is also getting caught up a little bit in nomenclature and like, okay, what are we calling things? And I think that's one thing, but I think the broader trend around how agents access systems of record, I think, is the bigger point.

• 5:48Speaker 03

  Yeah, yeah. And from my understanding, it's also, I mean, it could also apply to something as simple as a chatbot. You know, it's not necessarily just an API or an MCP server. You could be, you know, Salesforce acquired Slack a couple of years ago, and it could be something as simple as you sort of interacting with a CRM via a chatbot.

• 6:07Seema Amble?

  Totally. And I think I read somewhere that there has been like a 300% increase in Slackbot, Slack agent usage.

• 6:13Speaker 03

  Yeah.

• 6:13Seema Amble?

  Which is essentially saying that you don't need to log into the Salesforce interface to get the data.

• 6:19Speaker 03

  Yeah.

• 6:19Seema Amble?

  Or whatever data it is. So, yeah, it's essentially, again, all these are agentic ways of accessing it versus the human needing to go in, log the data, or from a read perspective, go back and see, okay, here's this opportunity, here's what happened, and look at themselves. And so, that interface is less relevant.

• 6:36Speaker 03

  Yeah. And Stephen, do you have anything to add here just on the kind of like definitional territory that we're covering right now or kind of this discussion?

• 6:44Steven Sinofsky?

  Well, sir, I mean, like, we're in definitional hell right now, where-

• 6:48Seema Amble?

  This is true.

• 6:49Steven Sinofsky?

  -is part of a new wave of technology is you make up a lot of new words for things that you kind of did before. And that's just a natural part of technology evolution. But I actually think it's super important. Like, first you have this agent, which as far as I can tell right now, is also a new word for a program that takes a very long time to run and might not finish. And that's the best branding ever, is to call a program that takes a really long time, which we used to call a bug, is now like the coolest new feature ever, and it's just now an agent. But in seriousness, the most interesting way, I think, to think up what you're really

• 7:30Steven Sinofsky?

  talking about doing differently between an agent and an API is really, what are you actually doing? What is the agent itself doing? Is it looking something up? Because that's actually a pretty lightweight thing that all systems are pretty good at. And in fact, many, many of the newly announced headless agent APIs are just lookup. You basically have a new interface to the old way to look something up, which is a lot more forgiving, a lot less UI goo and stuff like that. Then there's, I want to do something. And that's where you get into very interesting issues over, well, if you do something, you have to be impersonating a specific person, you have to have their credentials. Is it another paid seat?

• 8:15Steven Sinofsky?

  Is it the same paid seat? You have all these interesting enterprise software issues that come up if you actually want to cause a change to a system of record. And then there's the third thing, which is analyze. And so, analyze is more than look something up. It's actually look up a bunch of stuff. It often involves multiple systems. And that seems very, very tuned to an agent because you're not time bounded, you can spend energy iterate, you can route it to different models and get different answers back and compare them. But it's also where hallucination really is a huge issue. Because if you're going to go and analyze something, you actually need a way to verify

• 8:59Steven Sinofsky?

  that everything, every step of that analysis was correct. And so, I think it's super interesting and important when you look at headless and agent, which are conflated, you sort of have to figure out what you're talking about. Because we're on different places in the evolution, the learning curve, and the deployment of agents relative to sort of that three-way matrix.

• 9:22Speaker 03

  Yeah, I think this is actually a good lead up into a follow-up question, which is historically, what has made software sticky and how are agents starting to disrupt that? And I leave that to either of you to answer. Maybe you guys can both kind of debate about that.

• 9:38Seema Amble?

  Yeah, I'd say there are many things that made software sticky, but a lot of it had to do with the fact that it was built around like the way a human interacts, right? So, the UI was sticky because, you know, a number of times you had to read and write, the frequency of access, the downstream workflows, all of the like undocumented, what we call SOPs or standard operating procedures, all the stuff that happened around the software that got ingrained in muscle memory and process, and then external parties, et cetera. So, a CRM may be sticky because a sales rep needs to go in and out of it all the time. They're used to interacting with Salesforce. A lot of times when like new VPs of sales come into our companies, they, they're like, they mandate that Salesforce is there because they're used to using it, their teams are used to using it.

• 10:22Seema Amble?

  And then there's, you know, finance may rely on the Salesforce output for billing, and upstream marketing is going to rely on it as well. And so, there's these dependencies. And these all are driving stickiness. But I think the other piece too is there, you need one single set of truth, right? You need to know like an account is closed and who is working on it, and all of that needs to be logged in one place. And I think if you go from CRM to say an ERP or payroll, like that absolutely has even like legal reasons and compliance reasons why you can't have numbers that are not being, you know, tracked as cleanly and correctly as, you know, an auditor might like, for example.

• 11:06Seema Amble?

  So, anyways, this all drove stickiness there and durability because, you know, you were used to using Salesforce, the whole ecosystem was using Salesforce, and it was the default option. And maybe, you know, there's one or two others in the market. And so, I think historically, those were some of the things that were, were driving stickiness.

• 11:25Steven Sinofsky?

  Yeah, I mean, I, those are all exactly right. I think it's important to also consider that the most sticky thing you could do is actually collect money from a customer. And if you're collecting money, it turns out it's really, really hard for them to stop sending you money. And it's really hard for them to figure out what to do if they stop sending you money. And, and it sounds really trite, but the sticky software is software that's getting used somewhere. Then when you dig in and try to come up with reasons, well, it just depends on who you talk to in a company. You know, you talk to the HIPAA compliance people in some company and they're going to tell you, this is the software you have

• 12:05Steven Sinofsky?

  to use because it's like the most bestest HIPAA compliance software. If you talk to the administrators, you're going to hear about onboarding your users. If you talk to the users, you're going to hear about muscle memory and keystrokes or labor unions or whatever. And, and so yet to be, you really want to get the software sold and that's your fastest path to sticky. And after that, it's sort of, you know, a winner's tale over, over what caused it to be sticky. And, and in fact, the best thing about sticky is if you're the rep for a company that you've sold something to, and the company is threatening you like, hey, we're going to replace you, we're going to, you're just going to listen

• 12:49Steven Sinofsky?

  to them and you're going to find what's sticky. And if that works like three or four times across different accounts, then you've just told the tale as to what made the product sticky. It doesn't matter what the PMs or what anybody else thought of. It could be some crazy arcane thing. You know, I have stories of lots of sticky software and lots of arcane things, but like anyone who's ever tried to displace Microsoft Outlook as email, very quickly learned about delegate access and, and having calendars owned by multiple people and all of this crazy stuff. And like, I can tell you, there was no meeting where we said, okay, let's figure out how to make the calendar, the sticky part of Outlook,

• 13:33Steven Sinofsky?

  and make sure we handle recurring meeting exception handling well. And then you go and you find out, you know, General Motors isn't going to displace like 600,000 seats because of the calendar or some crazy thing like that. And so you can, it's really amazing in enterprise software, what causes sticky and how you can actually capitalize it on it when somebody threatens to take you out of the, out of the enterprise.

• 13:58Seema Amble?

  I think there's a really good point there. And two things, inertia is a really powerful force. And then I think the other thing is, yeah, nobody, when they're building the software and they're at like, thinking about like this rubric we put together and like tick, tick, tick, we got all these features that are going to do all these things. But I think the practical reality is also as software extends its tentacles across an organization and it gets ingrained in people using it and they've been paying for it for a long time, it just seeps into how people are doing things. And that's like hard to rip out.

• 14:27Speaker 03

  Yeah. Yeah. You even talk about this in your piece, Sima, where there's sort of all of these like invisible tacit sort of understandings about how to use different products or things that are embedded both within the software, but also within the people using them where it's just like, it does become hard after a while to, to extricate yourself from, from whatever ecosystem you happen to be in. And in fact, Stephen, I think you, you've even, you know, said the SaaS apocalypse is overblown. You've written an essay called the death of software. Nah, where you, where you, you know, emphatically sort of rejected this idea. So maybe if you want to, if you want to maybe just recount that piece a little bit for us.

• 15:11Steven Sinofsky?

  I mean, Sima co-wrote a post on, on SAP, which is sort of the ultimate, ultimate example of sticky software. I mean, there is nothing, the only software that's stickier than SAP is behind the scenes and it's the software that insurance companies wrote. And they wrote all this software like 50 years ago or 75 years ago. And if you ever, there's no replacing it. Like it, it just, it, in fact, whatever jokes come up about like businesses that are looking for COBOL programmers, it's to go and work on the insurance software that exists in, in every state of the union. And, and in many ways, what you're

• 15:50Steven Sinofsky?

  seeing, like with one of the biggest successes to date in Stripe, has been somebody actually went in and for the first time in two generations, coded up the software to collect money from people, which itself had previously been an unsolved problem on the scale of insurance. Because nobody put together the tax laws for every country, every, every jurisdiction, every locality, every border crossing, every currency exchange. Like it, it, it's mind blowing that. And so now that is the most sticky, like that is not going anywhere ever.

• 16:31Steven Sinofsky?

  Like it, it'll be like, we'll be doing this podcast with like great grandchildren, 100 years from now, talking about how sticky that experience was, just like I told like, oh, you didn't know this, but the software that runs Allstate is older than me and it's not going anywhere. And that's because the, these examples are ones that codified an external force. And that external force was the regulatory body that they embraced. The seamless examples of SAP, like it just codified a company. And, and so like, if you take SAP out of a large automobile manufacturer, there's no automobile manufacturer left

• 17:14Steven Sinofsky?

  or Walmart. Like the company just evaporates. Because the, the company is defined, not just by purchasing the software, not even by just using it, but by how that they codified the business rules into that product.

• 17:29Seema Amble?

  I think it's a good point to double click on because I think a misconception right now is that SAP, okay, well, you can just have, you know, Postgres database and APIs and then bam, like you can replace API, replace SAP. And that's like absolutely not true. I think partly, I mean, Stephen, I don't know if you want to elaborate on it or I'm happy to, but I think it's that piece around the logic and everything else that is, like that is captured in SAP is way, way more important than the fact that like, oh, this data just happens to be in this database. And there's a reason why also SAP like takes, you know, multiple years to implement and get, it's not because like, oh, you know,

• 18:09Seema Amble?

  yes, the system integrators are slow and part of it, but it's, it's customized to the way that business actually operates. And I think that's like an important part about why you can't just obscure away the software completely from and turn it into a database plus APIs.

• 18:25Steven Sinofsky?

  This, it's just so important because this is one of the things where like startups look at enterprise software and they think about it in terms of startup scale. And so they take something like mundane, like expense reporting and like, okay, well, we have 40 people and like, you know, one person can figure out expense reports for 40 people. Like you, you could hire a human and that, and be done with it. Like literally you come back from a trip, you dump the receipts in a bucket and one human rifles through them and the expense reporting problem goes away. Or you say, oh, forget the human, we'll just all take pictures of our receipts and OCR it and categorize it and the whole thing

• 19:03Steven Sinofsky?

  will go away. And, and that's fine until you have 100,000 people in 20 countries with different national laws and policies about business expense and all of this. And then you overlay corporate policies and, and the whole thing just, and then your business is just codified that way. And, and you can't replace it. Back in the, in the late 1990s, who now is sort of the godfather of enterprise software, Larry Ellison at Oracle, back then they were only a database company and entering the world of NetSuite and ERP and all this. But he went on a rant, a multi-year rant about how enterprise software was so stupid

• 19:44Steven Sinofsky?

  because everybody customized it. And he had this saying that just said, businesses should just stick with the 80% solution and they should just use whatever works like 80% of the time. And most enterprise people were like, well, A, you're just talking your book because your software only does 80% of what I need. But B, like, that's, that's just not how it works. Like if you take the auto industry and you just take the top 10 companies in autos, they, they all, you know, putting aside EV versus gas or whatever, they all just make cars, which is a lot of known technology with assembly lines and workers. Like what differentiates the, the companies and what differentiates them is how they operate and

• 20:29Steven Sinofsky?

  the internal processes to decide what car to make, how many more materials to buy, what currencies to hedge, how many people to hire, when to introduce a new product line. All of that is enterprise resource management and planning. And how is all of that done? It's all in SAP. So those companies are effectively run by people sitting in conference rooms, looking at SAP screens. And the difference between Ford and Toyota and General Motors and Daimler are just, are not just that they're looking at the same screen. It's that they chose which screens to look at, which customizations to make in those screens. And then they go

• 21:12Steven Sinofsky?

  and they buy steel and aluminum and wire and dashboards and radios from all the same places. And so it's a, I just think that people wildly underestimate the, the level of how of sophistication that customers apply to this software. Like when, back when we were first starting to get Excel used in companies, you'll laugh at this, Sima, like, like the, we used to do these little visits and we'd go visit bankers. And so we're sitting in Goldman Sachs and we're telling him about Excel is better than Lotus 123, blah, blah, that's super old. You don't even know what 123 is.

• 21:45Seema Amble?

  I know, I know.

• 21:46Steven Sinofsky?

  Trust me, it's old. And, and the guy at Goldman looked at us and said, I don't think you understand. We make more money from Excel than you do.

• 21:57Steven Sinofsky?

  And we're just sitting there like, what is he talking about? Like, it made no sense to us. And then we started to think about it. And it's like, well, we sell Excel to Morgan Stanley and JP and Chase and everybody else. And the Goldman was saying is their application of Excel is so differentiated. And that wasn't just people typing. They built add-ins, they wrote all this code, they defined their workflows. So there's this wild underestimation about like, you could vibe code your way into enterprise software.

• 22:27Seema Amble?

  I was at a dinner last night and there was someone there who was like the head of rev ops at a, I don't know, maybe growth stage startup. And his task, this is like a thousand plus person company, was to rebuild their Salesforce instance internally. And, you know, I think he's like, oh, well, you know, we know all the fields, we can import all the data. And I was like, that's not really the part that's tough, right? It's, well, how are you deciding what gets captured? How the whole organizational lines around it? And then who's going to maintain this also over time? I think that's like a piece that just falls off. You know, you can vibe code a CRM. We've all vibe coded projects that have like already gotten stale and no, we haven't touched again because it's painful.

• 23:11Seema Amble?

  And it's, it takes time and needs to adapt the business.

• 23:14Speaker 03

  And Simo, you've also written about how, you know, there are, there's an entire ecosystem of startups now that are just building on top of SAP. And that are, you know, like sort of building around all of the kind of headache inducing stuff. And, but still using SAP. So like to both of your points earlier, like these like legacy SaaS systems are just like so deeply embedded that the newer insurgents are just coming and they're, you know, building on top and around them rather than kind of like trying to rip out and get people to migrate completely.

• 23:51Seema Amble?

  A lot of what we're seeing AI being used today is, how do you make it like, I think the word is often used like conversational. Or like, how do you pull the information out and actually make it more usable? How do you retrieve the information from SAP without needing to go, you know, run a sequel query and get all the information? Or like, look at a bunch of screens? It's like, okay, well, if I want to be able to connect to Stephen's point or analyze, like, you know, three different sets of tables and different geographies. Like, can I quickly, you know, query that like a natural language way? Can I get reports automatically generated that are customized to me without needing to like go back through the like SAP customization process? And I think that is, that usability layer is, I think, indicative of what's happening now with software in general,

• 24:35Seema Amble?

  which is accessing the UI is optional. And like going back to the Slackbots point, like you want the information delivered to you versus needing to go to the UI. But the data and the business logic inside, either it's SAP or something else that's being replacing it, that's building it. But that all still needs to exist one way or another.

• 24:57Steven Sinofsky?

  That's, that's an incredibly important point is for, for folks to take away, which is that the, the biggest thing about enterprise software is it almost always does what somebody wants it to do. They just don't know how to make it do that. Like, there's no report that SAP can't generate, no graph, no chart, no analysis or whatever. But you, you just can't figure it out. Or maybe it's configured so you don't have permissions or something. So the two, the way to think of it is in enterprise software, the two most frequently used features exist in no enterprise software natively. It's export to Excel and export as CSV.

• 25:41Steven Sinofsky?

  And, or PDF, you pick. And so all enterprise software, the first thing they have to do that's missing when they show up and they do that first demo is the customers say, does it do export to Excel? Or does it do export to CSV or PDF? Because then you know you have an escape valve to do the thing that you couldn't do before on analysis. And, and so what's so cool about where we are today is that now with, with the language models, you have this incredible way to actually consume those in much easier than you could before. If you think about PDF, like the old way used to be like, okay, I want to figure out like exception handling.

• 26:22Steven Sinofsky?

  Some report that, that my system emits, you know, declined expense reports or whatever. But I want to do it over some weird time period or across different currencies that it doesn't handle. Or some weirdness that you can't figure out the UI. So now you can export them all and take these 20 PDFs and put them in a model and do a bunch of analysis that you couldn't do before. Or if you did, it was all copy and paste in this mundane thing. And to something that Simo wrote about, these sort of, I don't remember the word he used, but these ad hoc business processes are the ones that really become the most interesting. Because they're interesting because that's how a business runs. But they're also interesting because those are the next products.

• 27:07Steven Sinofsky?

  Like, those are the next companies that people start. You know, CRM used to just be a spreadsheet. Like, if you were in a business and you were an account manager and you kept track of your accounts, you just kept track of it in Excel. And then a company got started to do that. It wasn't, it wasn't Salesforce first, it was the predecessor called Siebel. And, and then like people are like, oh, we should make a whole company that does this. And that's what, that's what some of these apps are that you're seeing using language models and interfaces that are chat to SAP or to Salesforce, they, they are just trying to take advantage of what the LMs are really good at, which is synthesizing and orchestrating unstructured information.

• 27:51Seema Amble?

  Yeah, I think we also forget that like Salesforce is like really an enforcement mechanism for like the, the go-to-market team, which is like, okay, are you collecting all of the information that you need to? And of course, we can talk about Salesforce hygiene, et cetera, as a separate point. But like, do you have all, you know, is the human doing the, getting the data to then have, like to, to then, you know, capture the state of the business? Which, okay, but I think if we like now switch to the agentic world, you know, I think, again, we can talk about what agent means. But imagine there is an agent that needs to do outbound calling or outbound messaging. They want to be able to retrieve that information.

• 28:33Seema Amble?

  They don't really care about how the fields are organized or, you know, how many clicks it takes. But they do want to be, they still need to access that information. But then the second piece they need is this context thing. So we've talked a lot, I think, I feel like the internet has talked a lot about context graphs over the last six months. But what is that? That's all like the exceptions. What do you do, what do you, how do you handle certain cases? It's the edge cases and the permissioning and all that stuff that needs to be, and all the policies that are not necessarily in the fields of Salesforce. And so for the agent to then go, go back to this 80/20 thing, the agent can, you know, extract all the information, send an outbound email based on the information that's in the CRM around the person and their persona and what they do and all that.

• 29:17Seema Amble?

  But then like, okay, how do you deal with a case, one case versus another and how they respond? And it's like, oh, well, normally if it's a person who's in Asia, we respond this way. But if it's a person in the US, we respond this other way. That's not captured in Salesforce, but that's, that was in someone's head. And so that's the context that I think is really important now for agents to be able to act on behalf of this data.

• 29:42Steven Sinofsky?

  Well, that's true. I mean, for Salesforce in particular, that's incredibly important because I've never met a salesperson, an account manager, an account executive, who thinks that the default is the right answer for anything with their account. And like, even if they get the Japanese language right, you know, oh, it's spring and the birds are chirping. And, but you're, you're overdue on your payment, your license count is wrong. That even if you do that correctly, the rep is going to want to handle it in their, in their specific way. And, and I think that this notion of exception handling is just the root of the challenge with, with agents, which is almost everything interesting in an enterprise is an exception.

• 30:23Seema Amble?

  Yes, yep.

• 30:24Steven Sinofsky?

  Like that, like all the people are about exception handling. It, you know, it's basically like spend 15 minutes at McDonald's and watch people start in the kiosk and give up. And then go watch what they really want. And they're like, well, I wanted a McFlurry, but I wanted two flavors and mix them together. And that's not in the, and it's always the exceptions. And everything about automation in enterprise is handling exceptions. It just is, it's the strangest thing like, but you know, enterprise pricing is a great example. Like, how much is it per seat? Well, you have to call us. Well, you call, then you talk, and then it's still an exception.

• 31:01Seema Amble?

  Yeah, and that's exactly right. So these exceptions aren't, they're not captured anywhere right now. Now, I think if you say, if there is, you know, a voice agent that is doing, let's say, compliance check calls for freight, as one of our portfolio companies does, they are now collecting the exceptions through their voice agent and getting some of that context. Or if they're, we're looking, we can talk about computer using agents, if you're observing humans and how they are clicking through software, how they are responding to things. And now we have this ability now to, you know, not only record data or like record interactions, but then process it via LMs. Then you're able to start collecting some of this context. But it's a lot there. I mean, as Stephen was saying, it's, all the interesting work is around the exceptions.

• 31:45Seema Amble?

  So it's not like, okay, you know, three days later, we've, we've got it all, we've got all the context. Because also like sales cycles take a long time. Each exception isn't like handled with the frequency that you get the data immediately, right? And so you have to feel comfortable, you have to get to that point where you're like, okay, we've, we've observed enough interactions to actually capture, to understand the exceptions. And then on the sales side, the buyer trusts that, that, you know, this piece of software can actually, you know, has captured all the context to handle it.

• 32:16Steven Sinofsky?

  Well, I'll just add to building on that because I think it helps us to go back to this notion of headless. And what are both the challenges and the opportunities? Because of course, if you're an engineer, which almost everyone talking about what's going on in the world in AI today is, you think headless and API, agent API, just interchange them. And so you, you think, oh, well, it's code, I can write the business process down. And, and the problem that you hit right at the beginning is, is that if you're not an engineer, you can't even explain the process that you use to resolve a customer issue. You, and in fact, it's sort of very interesting to watch Amazon really do some of the best work on this because they really, really don't want to have humans.

• 33:01Steven Sinofsky?

  Like that you, you, you can't call Amazon for anything. Like, it's just hopeless. And so what they're doing is they're learning with everybody, like the best way to automate something. And it's their religion, it's their core principle, which is you just decide it in favor of the customer. You know, oh, they sent the wrong thing. So you go to the chatbot, you tell them the chatbot understands that you got the wrong thing and it just sends you a new one. And I think that that's so interesting compared to sort of old school exception handling. And then they use the data to go and improve the internal shipping and handling and warehouse process. Maybe it's the product description, a zillion other things or reviews.

• 33:43Steven Sinofsky?

  And, and so I find that's what I find so interesting about the capabilities of AI is that it's driving a different definition and different behavior at companies about how to handle exceptions. And I think when we get through sort of the 1.0 version of this, we're going to get to a new version where people are like comfortable letting AI do or decide things because they realize it's adding a level of predictability and repeatability to their enterprise.

• 34:11Speaker 03

  It is, it's funny to think that maybe customer service gets worse in the short term because things stop getting default decided in favor of the customer. You know, like suddenly, suddenly you actually have to defend your case again instead of like you being, you know, reship the, the Sensodyne toothpaste like you, you feel like you were owed. But I think, I think maybe then more generally, it, it sounds like you're both saying that automating the long tail is still kind of the hardest thing about, about all of this. Is, is that true? Or would you say that there are other hard things that, that developers and founders also need to think about?

• 34:52Seema Amble?

  I think that's, that's part of it. I think there are a lot of other things around like permissioning and this is, this is all I think I could, you could probably lump it into the hard tail, but oh, sorry, long tail, but like permissioning is part of this, right? And like, you know, as you give people or give it, you know, API access, it's like, okay, so which, in which cases can people extract data? When, when can they write versus read? Like that all needs to be figured out over time as well. And like interactions also between agents, right? And I think if you go back to the idea of a system of record, right? There's, it's one, ideally one set of, one central repository of data that is the, the source of truth. Well, that now, if you're, have multiple accessing and writing to it, like who gets to access when?

• 35:36Seema Amble?

  And I think that's a, that anyways, these are additional problems that I think need to be solved, solvable, but will take time.

• 35:43Steven Sinofsky?

  Well, one of the things that happens in, in technology shifts is, you know, everybody knows the thing about, nobody understands exponential when it's happening. So you have to be very careful to extrapolate and end up extrapolating linear when something exponential is happening. But the same thing happens with productivity or an analogous thing happens with productivity, which is people look at the existing body of work that happens today and they say, okay, how do we make that easier? And then all of a sudden, there's all this fear that we're going to automate everything away, that everything is just going to become an API, which developers and engineers say, oh, that will be easy. And then we'll be in this Nirvana world where everything is automated and easy and predictable.

• 36:25Steven Sinofsky?

  But they forget that productivity drives new scenarios. And so the minute that you can get something easier with automation and you can actually automate it, which I do think is happening right now with agents and with, with language models, well, then we're going to dream up a whole bunch of new stuff to do. Like I, I just mentioned this, this loop that Amazon must be in on customer service. Well, they got rid of all the phone people and the phone experience that would be miserable to do a return and the, the challenge response and the fighting and like, can I return this? And do I have to package it up or will you just ignore? Like toothpaste, they don't want it back. Like that's a, an pioneering invention by Amazon is like, you know, if somebody gets the wrong consumable, we just don't want it.

• 37:11Steven Sinofsky?

  Like they're poisoning it, they use part of it, it's cheaper to just have them throw it away. Well, that never happened before. Like you used to have to actually bring spoiled food to the supermarket and show it to them. And so they've fixed that level of productivity, but now there's this backend that's just out there constantly figuring out how to have it not happen again. And that now they need a new level of analysis, a new set of tools. And the long tail got no shorter, it just got longer in a different way.

• 37:39Seema Amble?

  Yes.

• 37:40Steven Sinofsky?

  And, and I think people forget that that's how innovation is this constant reinvention. And it's, it's a growing pie, not a static pie. And, and all the negativity around AI comes from just thinking that the work to be done is this fixed thing that takes N people and M amount of software. And we're just going to replace N people with M plus five. And then we don't, we're done. There's no jobs anymore. There's just an agent running. And that's just never going to happen. Like legal is a great example of this, like where people do contracts and they think that the law is going to help contracts get, get done quicker without lawyers.

• 38:20Steven Sinofsky?

  Except I can assure you, contracts will get longer and more sophisticated and encompass way more sets of scenarios than a person ever could.

• 38:29Seema Amble?

  And there'll be more.

• 38:30Steven Sinofsky?

  And that's going to create a whole.

• 38:31Seema Amble?

  More litigation around it and that creates a whole ecosystem.

• 38:33Speaker 03

  And more deals.

• 38:36Steven Sinofsky?

  Look, there's the, the now apocryphal, semi-apocryphal, famous example of radiology, which is a correlation, not a causation, but radiologists all love, love AI. And now we are having a radiology shortage. It's not, it's, there's a lot of reasons, it's complicated, but it, it, it just shows that the innovation wasn't static. And, and the market for the demand wasn't static. And, and so I think that a lot of what happens just in the micro at the enterprise level is the minute you automate the most mundane thing and think you have it all squared away, whole new things appear. Like actually like expense reporting is a really good example. You know, first there's nothing, then people figure out how to like do spreadsheets and

• 39:18Steven Sinofsky?

  then people figure out like, oh, now we have a whole system, we can analyze it. And now all of a sudden, business travel, you get ahead of the curve and you're like, well, now let's just use miles for business travel. Let's, you know, route our travel requests to the best prices we can get at any given moment, rather than just default to one carrier. Let's use a specific credit card for business travel that buys us a bunch of different added benefits that we know matter to our patterns of travel. And so suddenly, like there was a bigger job called business travel analysis that takes way more people than just booking the flights, which everybody can just do on their own.

• 39:57Seema Amble?

  There, there's always another layer of analysis on top.

• 40:01Steven Sinofsky?

  Always there, but the analysis then drives new processes and new behaviors that themselves differentiate companies. Like business travel is a, to stick with that example, is a huge sink in most companies. It's just a giant expense hole that they wish they could shrink. But once they can tie it to how things perform in their company, then it's more than just expense moderation, it's actually figuring out performance optimization. And figuring that whole thing out becomes like a different kind of job than just booking travel and analyzing expenses. It just becomes this whole remote work optimization tool. And then it's a different thing.

• 40:42Seema Amble?

  I think the other thing interesting, not to double, you know, to spend too much time on business travel, but I think it, it also ties their, the physical and like digital worlds. Like there are always things that there will be humans doing. You know, maybe it's not back office TPS reports, but like salespeople will be closing deal, there will be human interaction to close deals. People will be getting on planes as a result. And maybe they aren't spending as much time entering data into Salesforce or doing things along the way, but they will, there, there will be these humans doing online and offline world work. And I think that actually is something that, you know, there will always be a data exhaust from things to capture, optimization that needs to happen.

• 41:27Seema Amble?

  And that, that isn't going away either.

• 41:29Speaker 03

  Yeah.

• 41:30Steven Sinofsky?

  Well, I think open source software development is actually a really good example of this because like the hardest thing in software development is, you know, you have to be finished at some point so that everybody knows this is a stable release and can go build on it. And the art of finishing is this long tail of like not changing the code.

• 41:51Steven Sinofsky?

  And, and there's no API for that. Like developers think there, don't, they, developers don't hesitate to think there should be no API for that. They, they, they can think of a way to automate it with voting and with a discussion that has sentiment analysis or whatever, but they, you still need a bunch of people to concur over a decision to fix or not fix something. And yet they'll, those same people will just say some other business process, like closing the books for earnings, that should just be an API. And it's actually the literally the same mental model. Like, there's a bunch of stuff and we're deciding when to close the books and what sales to account for what and where. It's fixing a bug.

• 42:34Steven Sinofsky?

  And there's a story around it, a narrative, and we have to explain it to our boss. And if something goes wrong, we need a trail that explains who did what. And so so much of what a business really is, it are just the people deciding things. And all that software does is it up levels, abstracts and changes what they decide and how and what tools they use.

• 42:58Speaker 03

  The the other sort of follow up to Seema's point is like, it's the best case for just recording everything you do. Like all like just voice recording everything you do to like capture if if people are, you know, going and flying and closing deals in person. Make sure the software or the LLM can kind of capture everything that happens at all times. Obviously, not advocating for, you know, full panopticon, but.

• 43:24Seema Amble?

  But that is a contest gathering.

• 43:25Speaker 03

  But exactly, exactly.

• 43:28Seema Amble?

  And whether it's like, you know, recording and, you know, conversations or taking emails and, you know, written artifacts and ingesting them. This is all that is the way that the world is moving.

• 43:38Speaker 03

  Exactly.

• 43:39Seema Amble?

  So, yeah.

• 43:41Steven Sinofsky?

  Well, it's also to your earlier point, you know, expertise exists in this cloud in an organization. And and it is the the untapped resource of the modern era. And Aaron Levy at Box has done the the most eloquent job of explaining repeatedly the the assets that exist in all of these, you know, Word and Excel documents strewn throughout a company. And it's actually very, very hard to to understand which documents are important, which ones to believe. And part of being in a company and having a culture is really knowing the answer to that. And and it's super interesting to watch the customers at Box, Newsbox, to to to actually answer those questions.

• 44:25Steven Sinofsky?

  You know, which are the sales PowerPoint presentations that are actually working? Which are the spreadsheets and the models that people actually rely on? And and I think that that AI is the first thing to come along that really taps into that unstructured information in a company.

• 44:43Speaker 03

  Yeah, I think before we wrap up, it might be good to to visit the sort of more immediate history and then the more far away history of kind of what what headless software even is. I know, Stephen, you wrote last year a piece in reaction to to the rise of MCP servers. And in that piece, you also related it actually to sort of like early Microsoft litigation that the that the Justice Department levied against them. And and part of the argument was that Microsoft had a lot of products that could be categorized as middleware.

• 45:24Speaker 03

  And I'm just kind of curious, you know, in all of these different software waves that you've witnessed, kind of, in what ways is history rhyming and repeating? Maybe not on the litigation side, but on the, you know, product level.

• 45:38Seema Amble?

  That part will continue too, yes.

• 45:41Steven Sinofsky?

  Yeah, it's it's super interesting. You know, and I I I love Seema to opine on on where she sees things going with startups in this regard as well. So I'll go quick. But the the real thing with MCP is it's very much like everything we're seeing now is that it so much of it is driven by an engineering view of what would make for a good software architecture. And very little little of it is being driven by sort of the using Seema's, the physical reality of of the world. And so, of course, if you're an engineer and you would love to have like every tool you want to use to have a very clean API, preferably like a command line interface that pipes text in and out would be perfect.

• 46:25Steven Sinofsky?

  But that turns out to like not be how the world wants to work. There are many, many reasons why it doesn't want to work that way. Seema touched on many like security and compliance and things like that. But the reality is, is that no software wants to be disintermediated by some other layer above it. Like, nobody wants to to just be put in a corner and said, your job is to just store the this SQL format for expense reports and do nothing more. And then we're going to use you only for that. And then, by the way, we're we're piping you through to some other tool to analyze expense reports. Because that's a that's not a growing business, that's a decaying business. And and so it

• 47:10Steven Sinofsky?

  this whole notion of like, everybody is going to be perfectly content to be abstracted by some, you know, benign layer in the middle. It it just doesn't really work that way. And and it's because customers actually do not want to assemble their scenario from a bunch of different providers. Because all it takes is your system will only be as stable as the most unstable part of that. So if expense support company goes out of business, you're you're completely out of luck. So you want your expense support company to be thriving and doing more stuff, even though you in your head, you're like, I wish they would just stop. I don't want any more from them. It's getting complicated. Oh, they just did a UI reworking. That's driving me crazy.

• 47:54Steven Sinofsky?

  And the flip side is that those companies, they they're not just going to sit there and decay. And and they're going to look to the left and they're going to look to the right. And they're just going to do the stuff that they see people using with their product. And so SAP, the example Seema used, we're seeing this whole ecosystem grow up. And SAP is just going to do those things. And that's the now, not all of them, and most of them, they're not going to do very well. In fact, just before this, I was talking to somebody and we reminded them that in most giant enterprise companies, they view just a tie with some competitor as a win. Because they'll just bundle it into their existing thing and give it away.

• 48:35Steven Sinofsky?

  And so it's but this middleware layer, it's always, always very unstable. It looks great in a network hierarchy diagram of the OSI levels of networking, but it's just never that stable.

• 48:48Seema Amble?

  Yeah, I think two things I'll add. So one is, yeah, the the practical realities, like even go back to the Salesforce example or like Workday. Workday has had APIs that you could work with. But can you really actually extract all of the data out of Workday in a like clean way and just operate without using Workday? No, Workday makes it extremely difficult to actually like get access to the documentation and work with the like, and they don't, they don't expose all the info. Everything about the API, to use the API example, is this is, you know, analogous to what we're seeing now, which is that it makes it a dumb database, right? The and so they're not incentivized to do that. So I think what we're seeing is there's three paths in for in front of you.

• 49:31Seema Amble?

  One, if if you were a like a consumer or like a business that's looking to buy software. One is, okay, I take Salesforce and I either turn on Agent Force or build all my agents on top of it and then treat Salesforce as kind of the the just the backend. I think to what we just talked about, some of that will work, but some of that will also not work because Salesforce doesn't want you to want that to be, you know, to they don't want to be just the data in the background, right?

• 49:59Seema Amble?

  And so, you know, I think that that there will be mixed mixed results around that. And I don't have, I'm not bullish on the incumbent software building great agents on top. There's option two, which is you just totally DIY it. You have the most control in that situation. However, I think to everything we just talked about, that's really hard, right? Like, you have to rebuilding true enterprise software. And I think for a startup building rebuilding a CRM, much easier for rebuilding, you know, CRM for a Fortune 500 business. It's it's a lot of business logic to capture. And you're also trying to like do open heart surgery while like the patient is like alive, right? Or, you know, whatever one you want to use the analogy.

• 50:36Steven Sinofsky?

  Well, hopefully they're alive.

• 50:37Seema Amble?

  Yeah, yeah, yeah, yeah. Yes, yes, yes, of course, if they're alive. But I mean, you're like taking the engine out mid flight, whatever you want to say as the analogy. That's that's really hard. You have to get the like practical realities of permissioning and collaboration and all that, right? Then there's the third option, and I think this is why we are continue to do what we do in investing in in AI software is because there is a reason that like, you know, agents can continue to be built. The data can be sucked in and built in the background. A lot of what we're seeing right now is things that are working alongside an SAP or a layer of visibility on top that is enhancing the experience and allowing the business user to then run agents on top of the existing data they have.

• 51:20Seema Amble?

  And not but also not like throw out all of the the logic they've had in the background. And then I think also we create a new system of record, like voice agents are collecting new data. Recordings are correcting, collecting new data, transcription, ingestion of documents, all of that documentation is pulling in. And maybe, you know, one day these AI startups will replace the systems of record in the backend. But they are doing so in like a systematic way of observing how the business is operating.

• 51:47Speaker 03

  I guess I guess to close this out, though, Seema, you you sort of just touched on this. Where where are we really seeing the biggest opportunities for startups right now? I think, look, a lot of this, yeah, what I was just saying, it's it's it's doing the things that the incumbents are not doing right now, which is.

• 52:06Seema Amble?

  Which is going from a layer of collection of data and into how do we take action on top of it, right? And so take the CRM example, right? It's like, we're not just logging all the like call information, but then now I'm providing the intelligence back around, okay, how do I prioritize leads? Which accounts should we work on? What have, what has risk of churn? Flagging all of that and then like sending the outbound, right? And so, and, and, and part of that is creating this agentic loop, which is you now, as the agent sends the outbound, sees the response, you're understanding, okay, A, what works? What didn't? What, how did people respond? And then B, you're also collecting like benchmark data too. I'm like, okay, this type of response is most effective in these cases.

• 52:50Seema Amble?

  And in Asia, we should be using this language, you know, type of opening versus in Europe, et cetera, that sort of stuff. You're now agentially collecting all of that. And that's like an interesting data exhaust. So I think that's another area. And the third area I just would flag too, is we talked about this like physical realities, but the other part of physical realities is a lot of the vertical software that builds for the, you know, the physical world, actually. And that is a really interesting set of data. That's not, it's like hard to capture, has been hard to capture historically. And, you know, you will have to continue to pull together things that can be kept, you know, agents have been able to operate on software, but then also what humans are doing out in the field, machines are doing out in the field and pulling that back in. So like construction, manufacturing, all of that.

• 53:36Steven Sinofsky?

  Well, the universal truth for enterprise software is the, the most difficult thing to do that happens to be the dumbest is to attempt to just compete head on with, with an existing category. And, and by head on, I mean, not just the same category, but doing it the same way. The biggest opportunity right now is always, always to look at the existing sort of mental map of enterprise categories and be in between two established players. Because the thing that you know, right now during a massive technology shift is the one thing that established players won't do is disturb their existing product line and go to market.

• 54:20Steven Sinofsky?

  So they absolutely will just be bolting AI on top of their existing product. They, they won't be getting rid of it. They won't stop working on it. They won't do anything to, to break it. They're just going to try to weather this technology storm by sort of power through it, powering, powering through it. And so your opportunity in a startup is to just look at two big players who are bolting AI onto the side and exposing some existing API as an agent or whatever, and just aim for the middle and do things in an, in the new way and in a new way exclusively. And by not attacking head on, you don't show up at every single customer and have them go, you know, well, you need to these, do these 8,000 things before you even enter

• 55:05Steven Sinofsky?

  the door. Instead, you have an equally difficult question, but when you're in control of it, which is, why do you even exist?

• 55:13Steven Sinofsky?

  And, and that, but you, that's your own question. You, you don't have to answer to a, a series of 20 year frameworks that, for a 20 year old framework that got created to answer a bunch of questions that aren't even relevant anymore. And, and the best example of this is, is, is HTTP and HTML. Client server existed, but the reason that those took over was not because it did all the things that client server did. In fact, it did none of them, but it implemented that concept in an entirely new way. And so the web exists in spite of the fact that legacy vendors had a trillion dollars invested on how client server should work.

• 55:54Seema Amble?

  Well, and I, I would say the other piece too, it's not just to between two legacy vendors, but I think now there's like a layer of translation between two different functions within an organization too.

• 56:02Steven Sinofsky?

  Oh yeah, yeah, for sure.

• 56:03Seema Amble?

  Software has always sold like, oh, I'm selling into just, you know, the sales team or the finance team, but then there's like these handoffs and which is now the context, right? But on bills and deals and like, that actually also presents an interesting opportunity. So the last question I have, which is for Stephen, is, um, so network effects is this thing we always talk about on the consumer side and it's a great, you know, source of defensibility. No enterprise software business, as far as I can tell, has successfully, you know, implemented network effects, but you could argue that is a good source of durability over time, right? And I think Salesforce has tried this in, in, in a couple of ways in the past, but do you think that like enterprise software will start entering the, the, you know, the

• 56:48Seema Amble?

  field of network effects in terms of like, okay, we're going to have both buyers and sellers on our CRM and therefore be able to like mediate these transactions or like, yeah, I'm curious to get your take on that.

• 56:58Steven Sinofsky?

  Well, certainly, network effects outside of a company are extremely difficult for a bunch of compliance and security reasons. But, but the biggest network effect in enterprise software is inside of a company. And we're seeing that happen now with, with just chat. Like all of a sudden you're seeing that it's, it's so incredible to be at this dynamic that, that almost felt like the good old days when some very motivated person, like most people who work in enterprises, it turns out are not like super interested in making their job better. They actually just want to go to work, get paid and go home. And they don't come to work every day going, ooh, how could I make my, how could I streamline my tasks? They just want to not mess it up.

• 57:41Steven Sinofsky?

  That is a lot of the world. But there was a small set of people, like those bankers at Goldman Sachs, that were like, how do I do more deals faster, better, more clever models? And so they were using Excel when, when the other bankers were using one, two, three. There's actually floating around on the internet is this old commercial for Excel. The launch, this launched TV ad from the night, from the late 1990s where, or sorry, the late 1980s, where the first Excel spreadsheets were being used. And it's a person sitting there with this monstrous laptop that weighed like 12 pounds in an elevator, trying to use it. I, I'm laughing because of course they were trying to not run out of battery life in the elevator ride, which was invariably the case.

• 58:23Steven Sinofsky?

  But all, and all of a sudden this crowded elevator or a bunch of people in, in these 1980s ties and 1980s, wearing glasses, looking at the spreadsheet going, what are you doing? How are you doing that? And getting all excited. And fast forward to 2025, and that's exactly what happened with chat. Like, in fact, I had a friend at SAP that was, was writing like a SAP white paper about something. And, and I, I just asked them, tell me what questions you're trying to answer. And I did the prompts and sent them back a white paper. And I'm positive I kicked off some sort of viral loop, not technically a viral loop, but some sort of network effect viral loop inside of her team.

• 59:08Steven Sinofsky?

  Because like, all of a sudden people were seeing how to make their job better and it's accessible to them and they're doing it. So I, I think, and to your point, Seema, like this idea of, of a tool that enables two functions to talk together that couldn't before, it is golden. Like, that's exactly, like, that's literally what enterprise software integration is, except that's all manual brute force, higher accenture kind of stuff. And so if you have products that bridge this, and you know, Figma did a bunch of this with design and product development. And so if you can develop software that leverages AI in order to bring together parts of an organization that don't normally communicate, that's a whole, that's a new category.

• 59:53Steven Sinofsky?

  And we've seen that with things like IT budgeting, where IT and finance would end up with tools that ended up helping them both do forecasting and the cloud enabled that. And so I, I think that that's, that's a huge opportunity.

• 1:00:08Seema Amble?

  Well, I think that's also an amazing note to end on. Thank you so much, Stephen, for, for joining us here.

• 1:00:15Steven Sinofsky?

  Okay, Seema.

• 1:00:16Seema Amble?

  Yeah. And thank you. And thank you, Seema.

• 1:00:21Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.substack.com. Thanks again for listening and I'll see you in the next episode.

• 1:00:46Erik Torenberg?

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