Technology companies aren't entitled to their future success.
AI, I think will change the landscape of software and I think it will help some companies and it will really hurt others.
And so, when I think about what it means to build a company that's enduring, that is a really, really tall task in my mind right now, because it means not only making something that's financially enduring over the next 10 years, but setting up a culture where a actually evolve to meet the changing demands of society and technology when it's changing at a pace that is like unprecedented in history.
So I think it's one of the most fun business challenges of all time.
I just get so much energy because it's incredibly hard and it's harder now than it's ever been to do something that lasts beyond you.
But that I think is the ultimate measure of a company.
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Six months after Brett Taylor realized AI was about to change everything, he walked away from his co -CEO job at Salesforce to start from scratch. That's how massive this shift really is.
The mastermind behind Google Maps and the former Chief Technology Officer at Facebook, Look, Brett reveals the brutal truths about leadership, AI, and what it really takes to build something that endures long after you've reached the top.
Brett's led some of the most influential companies in tech and seen exactly what makes businesses scale, what kills them from within, and why most founders don't survive their own success.
In this conversation, you'll discover why so many companies are already on life support without realizing it—how first principles thinking separates the next wave of winners from everyone else, and the hidden reason most acquisitions fail.
We'll explore why AI is bigger than anyone suspects, plus the mindset shift that turns great engineers into exceptional CEOs.
Whether you're a founder, an operator, or simply someone who wants to think sharper, this episode will change how you see your business, technology, and the future.
It's time to listen and learn.
What was your first real aha moment with AI where you realized, holy shit, this is going to be huge.
I had two separate aha moments.
One that I don't think I really appreciated how huge it would be, but it kind of reset my expectation, which was the launch of Dolly in the summer of 22, is that right?
It might be off by year, but I think summer of 22, and the avocado chair that they had generated.
And I had been, well my background is in computer science and pretty technically deep.
I hadn't been paying attention to large language models.
I just didn't follow the progress after the Transformers paper.
And I saw that and my reaction was, I had no idea computers could do that.
And that particular launch, you know seeing a generated image of an avocado chair I don't think I extrapolated to what you know where we are now.
But it, for me, shook me and realized I'd need to pay more attention to this space and Open AI specifically than I had been.
I think I had, that was the moment I realized like I clearly have been not paying attention to something significant.
And then it was you know six months later coincidentally like the month after I left Salesforce, Chat GBD came out, and before it became a phenomenon though, it did so quickly, but I was already, you know, plugged into it and I was, from then on, you know, I could not stop thinking about it.
But that Avocado Chair, I don't know why, I think it was the, there was a bit of an emotional moment where you saw a computer doing something that wasn't just rule based, but creative and the idea of a computer doing something, creating something from scratch was, well it doesn't seem so novel, a few years later just blew my mind at the time.
One of the unique things about you is that you've started companies, you've been acquired by Facebook and Salesforce.
Inside those companies you rose up to be the CTO at Facebook, the Co -CEO at Salesforce.
talk to me about founders working for founders and founders working within a company.
Yeah, it's a very challenging transition for a lot of founders to make.
I think there's lots of examples of acquisitions that have been really transformative from a business standpoint.
I think YouTube, Instagram, BN2 are the more prominent that have clearly changed the shape of the acquiring company.
But even in those cases, the founders didn't stay around that long.
And that's maybe a little unfair, stick around for a little bit.
I think the interesting thing about being a founder is it's not just building a business, but it's very much your identity.
And I think it's very hard for people who aren't founders to experience it.
You take everything very personally, from the product to the customers, to the press, to your competitors, both inner and outer measures of success.
And I think when you go to be an acquired, there's a business aspect to it and can you operate within a larger company.
But that's intertwined with a sense of identity.
You go from being the founder of a company and the CEO of a company or CTO of a company, whatever your title happens to be as one of the cofounders, to be in a part of a larger organization and to fully embrace that you actually need to change your identity.
You need to go from being, you know, the head of Instagram or in my case the head of Quip to being an employee of Salesforce or going from being the CEO of FriendFeed to being an employee of Facebook.
And what I've observed is it's that identity shift is a prerequisite for most of the other things.
It's not simply your ability to handle the politics and bureaucracy of a bigger company or to navigate a new structure.
I actually think most founders don't make that leap where they actually identify with that new thing.
It's even harder for some of the employees too because most of the time in an acquisition, an employee of an acquired company didn't choose that path.
And in fact they chose to work for a different company and they, you know, the the acquisition determined a different outcome.
And that's why integrating acquisitions is so nuanced.
And I would say that having the experience of having been acquired before and having acquired some companies before when I got to Salesforce I really tried to be self -aware about that and really tried to be a part of Salesforce and tried to shift my identity and not be a single issue voter around Quip.
I really tried to embrace it.
I think it's really hard for some founders to do and some founders don't want to honestly.
You know, they maybe cash the check and that's the, it's more of a transactional relationship.
I really actually am so grateful for the experience of having been at Facebook and Salesforce.
I learned so much. But it really took a lot of effort on my part to just transform my perception of myself and who I am to get that value out of the company that acquired us.
How did it change how you did acquisitions at Salesforce?
You guys did a lot of acquisitions while you were there and you're acquiring founders and sort of startups and I think Slack was, well, you were there too, how did that change how you went about integrating that company into the Salesforce culture?
I'll talk abstract about, I'll talk about some specific acquisitions too, but first I think I tried to approach it with more empathy and more realism.
One of the nuanced parts about acquisitions is there's the period of doing the acquisition, there's sort of the period after you've decided to do it of doing due diligence and then there's a period when it's done and you're integrating in the company and sort of the period after.
One of the things that I have observed is that companies doing acquisitions, often the part of deciding to do it is a bit of a mutual sales process.
You're trying to find a fair value for the company and and there's some back and forth there but at the end of the day there's There's usually some objective measure of that influenced by a lot of factors, but there's some fair value of that.
What you're trying to do is, corporate speak could be synergies, but why do this?
Why is one plus one greater than two?
That's why you do an acquisition just from first principles.
It's often an exercise in storytelling.
You bring this product together with our product and customers will find the whole greater than the sum of it's parts, this team applied to our sales channel or if you're a Google acquisition, imagine the traffic we can drive to this product experience.
In the case of something like an Instagram, imagine our ad sales team attached to your amazing product and how quickly we can help you realize that value, whatever it might be.
I find that people, because there's sort of a craft of storytelling, for both sides to come to the same conclusion that they should do this acquisition sometimes either simplifies or sugarcoats like some of the realities of it.
You know, little things like, you know, how much control will the founding team of the acquired company have over those decisions?
Will it be operated as a stand alone business unit or will your team be sort of broken up into functional groups within a larger company?
And it's sort of those little, they're not little, but those I'll say boring but important things that often people don't talk enough about and you don't need to figure out every part of an acquisition to make it successful.
But often you can end up running into like, true third rails that you didn't find because you were having the storytelling discussions rather than getting down to brass tacks about how things will work and what's important.
The other thing that I think is really important is being really clear what success looks like.
and you know I think sometimes it's a business outcome, sometimes it's a product goal.
But I found that if you went to most of the like larger acquisitions in the valley and you, two weeks after it was closed interviewed the management team of the acquiring company and the acquired company and you asked them like what does success look like two years from now?
My guess is like 80 % of the time, you get different answers.
And I think it goes back to this sort of storytelling thing where you're talking about the benefits of the acquisition, talking about what does success look like.
So I really tried to approach it, I tried to pull forward some harder conversations when I'm doing acquisitions or even when I'm being acquired, since it's happened to me now twice, so that when you're approaching it, you not only get the, hey, why does one plus one equal greater than two?
Everything's gonna be awesome.
But no, for real, what does success look like here?
And then as a founder, your job of an acquired company is to tell your team that and align your team to that, and I think founders don't take on enough accountability towards making these acquisitions successful as I think they should, and it goes back to, again, a certain naivete, it's like you're not your company anymore.
You're a part of something larger, and I think successful ones work when everyone embraces, embraces that.
What point in the acquisition process is that conversation?
Is that after we've signed our binding, you know, sort of commitment or is it we should have that conversation before.
So I know what I'm walking into.
My personal take is it's not something you have. You have to get to the point where the two parties want to merge, you know, and that's a, obviously a financial decision, particularly if it's like a public a company, there's a board and shareholders, most acquisitions in the Valley are a larger firm acquiring a private firm.
That's not all of them, but I would say that's the vast majority.
And in those cases, there's often a qualitative threshold where someone's like, yeah, let's do this.
We've kind of have the high level terms, sometimes a term sheet, you know, formally, I think it's right after that.
Um, so where people have really committed to the, the key things, how how much value, why are we doing this, the big stuff.
And there's usually many, lots of lawyers being paid lots of money to turn those term sheets into a more complete set of documents, usually more complete due diligence, stuff like that.
There's an awkward waiting period there.
And that's a time, I think, where the strategic decision makers in those moments can get together and say, let's talk through what this really means.
And the nice part about having them for all parties is you've kind of made the commitment to each other.
So it's, you've, I think you have more social permission to have real conversations at that point.
Um, but you also haven't consummated the relationship, you know, and so, uh, there's a, the power imbalance isn't totally there and, and you can really talk through it.
And it also, I think engenders trust just because by having harder conversations in those moments.
You're learning how to have real conversations and learning how each other works.
So, that's my personal opinion.
So, you mentioned the board, you've been on the board of Shopify, you're on the board of OpenAI, you're a founder, what's the role of a board and how is it different when you're on the board of a founder -led company?
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I really like being involved in a board and I've been involved in multiple boards because I think I am an operator through and through.
I probably self -identify as an engineer first more than anything else and I love to build.
Learning how to be an advisor is a very different vantage point that I think you see how other companies operate and you also learn how to have an impact and add value without doing it yourself and it's a very, I really think, become a better leader, having learned to do that.
I have really only joined boards that were led by founders because, typically, I think They you can speak to them but I think that they sought me out because I'm a founder and I like working with founder led companies.
I I think the founders I'm sure there's lots of studies on this but I think founders drive better outcomes for companies.
There's a I think founders tend to have permission to make bolder more disruptive decisions about their business than a professional manager.
there's exceptions like Satya I think is you know one of the greatest not the greatest CEO of you know our generation and as a professional manager but you know you look at everyone from Tobi Lukey to Mark Benioff to Mark Zuckerberg to Sam at OpenAI and I think when you all have founded a company it's all your stakeholders employees in particular give you the benefit of the doubt you You created this thing and if you say, hey, we need to do a major shift in our strategy, even hard things like layoffs, founders tend to get a lot of latitude and are judged, I think, differently.
And I think rightfully so in some ways because of the interconnection of their identity to the thing that they've created.
And so I actually really believe in founder -led companies.
One of the real interesting challenges is going from a founder -led company to not.
And, you know, Amazon has gone through that transition, Microsoft has gone through that transition for that reason.
But I love working with founders, and I love working with people like Toby and Sam because they're so different than me yet, and I can see how they operate their businesses and I am inspired by it, I learn from it, and obviously, working for Mark at Salesforce.
You have, like, wow, that's really interesting, like, almost like an anthropologist. Like, what, why did you do that?
You know, I wanna learn more.
And so I love working with founders that inspire me because I just learned so much from them.
It's such an interesting front row receipt into what's happening.
Do you think founders go astray when they start listening to too many outside voices?
And this goes back to the, I'm sure you're aware of the Brian Chesky, the founder mode.
The founder mode. Do you think, talk to me about that.
I have such a nuanced point of view on this because it is decidedly not simple.
So, broadly speaking, I really like the spirit of founder mode which is just having deep founder led accountability for every decision at your company.
I think that that's how great companies operate and when you, you know, proverbially make decisions by a committee or you're more focused on process than outcomes, that produces all the experiences we hate as employees, as customers, you know, that's the proverbial DMV, it's like process over outcomes.
And then similarly, you look at the disruption in all industries right now because of AI, the companies that will recognize where things are clearly going to change, like everyone can see it, it's like a slow motion car wreck, everyone knows how it ends, you need that kind of decisive breakthrough boundaries layers of management to actually make changes as fast as required in business right now.
The issue I have not with Brian statements.
Brian's amazing, um, is how people can sort of interpret that and sort of execute it as a caricature of what I think it means, uh, you know, there was a, I remember after Steve jobs passed away and you know, um, I don't know, I've met Steve a couple of times.
I haven't ever worked with him in any meaningful way.
You know, but he was sort of, uh, if you believe the story is like.
Kind of pretty hard on his employees and and very exact in and I think a lot of founders were like mimicking that you know Done to wearing a black turtleneck and yelling at their employees.
I'm like not sure that was the cause, you know I think Steve Jobs taste and judgment through you know executed through that, you know packaging was the cause of their success and somehow and similarly, I think founder mode can be weaponized as an excuse for just like over at micromanagement and And that probably won't lead to great outcomes either.
And most great companies are filled with extremely great individual contributors who make good decisions and work really hard. And companies that are like solely executing through the judgment of individual probably aren't going to be able to scale to be truly great companies.
So I have a very nuanced point because I actually believe in founders, I believe in actually that accountability that comes from the top.
I believe in cultures where you know founders have license to go in and all the way to a small decision and fix it.
The infamous question mark emails from Jeff Bezos.
You know that type of thing that's that's the right way to run a company.
But that doesn't mean that you don't have a culture where individuals are accountable and empowered and you don't want it.
You know people trying to decide make business decisions because of what will please our deal leader you know which is like the caricature of this and so you know after that came out but I could sort of see it all happen, which is like, some people take them like, you know what, you're right, I need to go down and be in the details and some people will do it and probably make everyone who works for them miserable and probably both will happen as a consequence.
Totally, thank you for the detail and nuance there.
I love that. Do you think engineers make good leaders?
I do think engineers make good leaders, but one thing I've seen is that I think that, I really believe that great CEOs and great founders start usually with one specialty, but become more broadly specialists in all parts of their business.
You know, I think the businesses are multifaceted and rarely is a business's success due to one thing like engineering or product which is where a lot of founders come from.
Often your go -to -market model is important for consumer companies.
How you engage with the world in public policy becomes extremely important.
And I think as you see founders, you know, grow from doing one thing to growing to be in a real meaningful company like Airbnb or Meta or something.
You can see those founders really transform from being one thing to many things.
So I do think engineers make great leaders, I think the first principles thinking, the system design thinking really benefits things like organization design, strategy, and but I also think that you know when we were speaking earlier about identity I think one of the main transitions founders need to make, especially engineers, is you're not like the product manager for the company or the CEO and at any given day do you spend time recruiting an executive because you have a need?
Do you spend time on sales because that will have the biggest impact.
Do you spend time on public policy or regulation?
Because if you don't, it will happen to you and could really impact your business in a negative way.
I think engineers who are unwilling to elevate their identity from what they were to what it needs to be in the moment often leads to sort of plateaus in companies' growth.
So a hundred percent, I think engineers make great leaders and it is not a coincidence.
I think that most of the Silicon Valley, great Silicon Valley CEOs came from engineering backgrounds.
But I also don't think that's sufficient either as your company scales.
And I think that making that transition is all the great ones have is incredibly important.
To what extent are all business problems engineering problems?
That's a deeper philosophical question that I think I have the capacity to answer.
what is engineering.
What I like about approaching problems as an engineer is first principles thinking and understanding the root causes of issues rather than simply addressing the symptoms of the problem.
And I do think that coming from a background in engineering that is everything from process like how engineers do a root cause analysis of an outage on a server is a really great way to analyze why you lost a sales deal you know like I love the systematic approach of engineering one thing that I think going back to good ideas that can become caricatures of themselves like one thing I've seen with engineers who go into other disciplines is sometimes you can overanalyze decisions in some domains let's just take modern communications which is driven in social media and very fast paced, having a systematic
first principles discussion about every tweet you do is probably not a great calm strategy.
And so, and then similarly, there are some aspects of say enterprise offer sales that are rational, but they're human, like forming personal relationships and the importance of those to building trust with a partner.
It's not all just, you know, product and technology.
And so I would say, I think a lot of things coming with an engineer mindset could really benefit, but I do think that taking that to its logical extreme can lead to analysis paralysis, can lead to overintellectualizing, some things that are fundamentally human problems. And so, yeah, I think a lot can benefit from engineering, but I wouldn't say everything's an engineering problem in my experience.
You brought up first principles a couple of times.
You're running your third startup now.
Sierra, it's going really well.
How do you use first principles in terms of how do you use that at work?
Yeah, it's, it's particularly important right now because the market of AI is changing so rapidly.
So if you rewind two years, you know, most people hadn't used ChatGPT yet.
most companies hadn't heard the phrase large language models or generative AI yet and in two years you have ChatTBT becoming one of the most popular consumer services in history faster than has than any service in history and you have across so many domains and the enterprise a really rapid transformation the law is being transfer transformed marketing is being transformed customer service which is where my company CIRA works as being transformed, software engineering is being transformed, and the amount of change in such a short period of time is, I think, unprecedented and perhaps I lack the historical
context, but it feels faster than anything I've experienced in my career.
And so, as a consequence, I think if you are responding to the facts in front of you and not thinking from first principles about why we're at this point and where it will probably be 12 months from now, the likelihood that you'll make the right strategic decision is almost zero.
So as an example, it's really interesting to me that with modern large language models, one of the careers that is being most transformed is software engineering.
and you know one of the things I think a lot about is how many software engineers will we have our company three years from now what will the role of a software engineer be as we go from being authors of code to operators of code generating machines what does that mean for the type of people we should recruit and if I look at the actual craft of software engineering that we're doing right now, I think it's literally a fact that will be completely different two years from now.
Yet I think a lot of people building companies hire for the problem in front of them rather than doing that.
But two years is not that long.
Those people that you hire now will just be getting really productive a couple years from now.
So we try to think about most of our long -term business from first principles, everything from I'll say a couple examples in our business.
Our pricing model is really unique and comes from first principles thinking rather than having our customers pay a license for the privilege of using our platform.
We only charge our customers for the outcomes.
Meaning if the AI agent they've built for their customers solves the problem, there's like usually a pre -negotiated rate for that.
And that comes from the principle that in the age of AI, software isn't just helping you be more productive, but actually completing a task.
What is the right and logical business model for something that completes a task?
Well, charging for a job well done rather than charging for the privileges you doing the software.
Similarly, with a lot of our customers, we help deliver them a fully working AI agent.
We don't hand them a bunch of software and say good luck, configure it yourself.
And the logic there is in a world where making software is easier than it ever is before and you're delivering outcomes for your customer, the delivery model of software probably should change as well.
And we've really tried to reimagine what the software company of the future should look like and trying to model that in everything that we do.
That's brilliant. How do you think software engineering will change?
Is it you're gonna have fewer people or the people are gonna be organized differently or how do you see that?
How geeky can I get as geeky as you want?
I actually wrote a blog post right before Christmas about this.
I think this is an area that deserves a lot more research. I'll describe where I think we are today and smart people may disagree but a lot of the modern large language models both the traditional large language models and sort of the new reasoning models are trained on a lot of the knowledge that they're trained on.
As a consequence, even the early models were very good at generating code.
So you know, every single engineer at CIRA uses Cursor, which is a great product that basically integrates with the IDE, Visual Studio Code, to help you generate code more quickly.
It feels like a local maximum in a really obvious way to me, which is you have a bunch of code written by people, written in programming languages that were designed to make it easy for people to tell a computer what to do.
Probably the funniest example of this is Python, it almost looks like natural language.
But it's notoriously not robust. Most Python bugs are found by running the program because there's not static type checking, and similarly there's most bugs.
While you could run a fancy static analysis like most bugs show up simply at runtime because it's just not designed.
It's designed to be ergonomic to write.
Yet, we're using AI to generate that.
So we sort of designed most of our computer programming systems to make it easy for the author of code to type it quickly.
and we're in a world where actually generating code is going to like the marginal costs of doing that as going to zero.
But we're still generating code and programming languages that were designed for human authors.
And similarly, if you've ever like looked at someone else's code, which a lot of people do professionally is call the code review.
It's actually quite hard to do a code review.
You know, you're end up interpreting.
You're trying to basically put the system in your head and simulate it as you're reading the code to find errors in it.
So the irony now that I've taken things that are code programming languages that were designed for authors and now having humans do the job of essentially code reviewing, code written by an AI, and yet all of the AIs being in the code generation part of it, I'm like, I'm not sure it's great, But we're generating a lot of code with similar flaws that we've been generating before, from security holes to just functional bugs and in greater volumes.
And I think what I would like to see is if you start with the premise that generating code is free or going towards free, what would be the programming systems that we would design?
So for example, Rust is an example of a programming language that was designed for safety, not for programming convenience.
You know, my understanding is that the Mozilla project, you know, there were so many security holes in Firefox.
They said, let's make a programming language that's very fast, you know.
But everything can be checked statically, including memory safety.
Well, that's a really interesting direction where you weren't operating, like optimizing for authorship convenience or optimizing for correctness.
Are there programming language designs that are designed so a human looking at it can very quickly evaluate, does this do what I intended it to do?
There's an area of computer science I studied in college called formal verification, which at the time was turning a lot of computer programs into math proofs and finding inconsistencies and it sort of worked well, not as well as you'd hope.
But, you know, in a world where AI is generating a lot of code, should we be investing in more informal verification so that the operator of that could generate a machine can more easily verify that it does in fact to do what they intended is to do and could a combination of a programming language that is more structurally correct and structurally safe and exposes more primitives for verification plus a tool to verify could you make an operator of a code generated machine 20 times more productive but more importantly make the robustness of their output 20 times greater.
And then similarly, things go in and out of fashion, but test driven development, where you write your unit tests first or your integration tests first and then write code until it fulfills the test. Most programmers I know who are really good not despise it, but it's just like, it sounds better than it is in practice.
But again, writing code is free.
So writing tests is free.
How can you create a programming system where the combination of great programming language design, formal verification, robust tests because you didn't have to do the tedious part of writing them all.
Could you make something that made it possible to write increasingly complex systems that were increasingly robust?
Then similar to like the elephant in the room for me is the anchor tenant of most of these code generating systems are an IDE right now and that obviously doesn't seem as important in this world.
And even with code in agents, which is sort of where the world is going, it doesn't change the fact that like, you know, who's accountable for the quality of it, who's fixing it.
And I think there is a world where we can make reasonable software by just automating what we as software engineers do every day.
But I have a strong suspicion that if we designed these systems with the role of a software engineer in mind being an operator of a machine rather than the author of the code, And we could make the process much more robust and much more productive.
And it feels like a research problem to me.
It doesn't feel...and I think a lot of people, and for good reason including me, are just excited about the efficiency of software development going up.
And I want to see the new thing.
You know, I'm constructively dissatisfied with where we are.
It's so interesting that if software A .I.
is good enough to write the code, should we get enough check the code?
That's a great, great question.
But actually all you know, it's still funny to me that we'd be generating Python You know just because yeah for anyone who's listening right now has ever operated a web service running Python It's CPU and intensive really an efficient You know should we be taking most of the unsafe C code that we've written and converting it to a safer system like rust You know if authoring these things and checking it are relatively free shouldn't all of our programs be incredibly efficient.
Should they all be formally verified?
Should they all be analyzed by a great agent?
I do think it can be turtles all the way down.
You can use AI to solve most problems in AI.
The thing that I'm trying to figure out is like what is the system that a human operator is using to orchestrate all those tasks.
And you know I go back to the history of software development and most of the really interesting metaphors and software development came from in computing.
So, you know, C programming language came from Unix, and when these timesharing systems were really, it went from sort of punch cards to something that were a lot more agile.
Smalltalk came out of the development of the graphical user interface at Xerox PARC, and, you know, there was a, sort of a confluence of message passing as a metaphor and the graphical user interface.
And then there was a lot of really interesting principles that came out of networking and distributed systems, distributed lock -in sequencing.
I think we should recognise that we're in this brand new era as significant as the GUI.
It's a completely new era of software development and if you were just to say, I'm going to design a programming system for this new world from first principles, what would it be?
I think when we develop it I think it'll be really exciting because rather than automating and turning up the speed of just generating code and with the same processes we have today, I think will feel native to the system and give a lot more control to the people who are orchestrating the system in a way that I think will really benefit software overall.
Let's dive into AI a little bit.
How would you define AGI to the layman?
I think a reasonable definition of AGI might be that any task that a person can do at a computer, that system can do on par or better.
I'm not sure it's a precise definition, but I'll tell you where that comes from and it's flaws, but there's not a perfect definition of AGI in my opinion, or there's not a precise definition of AGI.
I'm sure there's good answers.
One of the things about the G in AGI is about generalization.
So can you have a system that is intelligent in domains that it wasn't explicitly trained to be intelligent on?
And so I think that's one of the most important things is like given a net new domain, can this system become more competent and more intelligent than a person sort of trained in that domain.
And I think that's sort of the, at or better than a person is certainly a good standard there and that's sort of the definition of super intelligence.
The reason I mentioned at a computer is I do think that it is a bar that means if there's a digital interface to that system, it affords the ability for AI to interact with it which is why that's a bar that's reasonable to hit.
I say that because one of the interesting questions around AGI is how quickly it does generalize and there are domains in the world that the progress in that domain isn't necessarily limited by intelligence, but by other social artifacts.
As an example, and I'm not an expert in this area, but if you think about the pharmaceutical industry, my understanding is the one of the main bottlenecks is clinical trials.
So no matter how intelligent a system would be in discovering new therapies, it may not materially change that.
And so you may have something that's discovering new insights in math, and that would be delightful and amazing.
but the existence of that system that super intelligent in one domain may not translate to all domains equally.
I just heard at least a snippet of a talk by Taylor Cohen, the economist, and it was really interesting to hear his framing on this about which parts of the economy could sort of absorb intelligence more quickly than others, and so I choose that definition of AGI, recognizing that there's not a perfect definition because it captures the ability of this intelligence to generalize while also recognizing that the domains of society might not apply with equal velocity even once we reach that point of a system being able to have that level of intelligence.
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When I think about what artificial intelligence is limited by, or the bottlenecks if you will.
I keep coming back to a couple of things.
There's regulation, there's compute, there's energy, there's data, and there's LLMs. Am I missing anything?
So you're saying the ingredients to AGI?
Yeah, there's limitations on each aspect of those things and they seem to be the main contributors to what's limiting us from even accelerating at this point.
How do you think about that?
Yeah, what you said is roughly all I think about it, I'll put it into my own words though I think the three primary inputs are data compute and algorithms and data is probably obvious but you know one of the things after the transformer model was introduced is it afforded an architecture with just much greater parallelism which meant models could be much bigger and train more quickly on much more data which just led to a lot of the breakthroughs with that's the L and L of just they're large.
Yeah. And uh, the scaling laws, you know, a couple of years ago, you know, indicated like the larger you make the model, um, the more intelligent would be an, at a degree of efficiency.
That was a tolerable.
Uh, and there we are, you know, there's lots of stuff written about this, but you know, there's in terms of just like textual content to train on, you know, the availability of new content is certainly waning.
And some people would say, I think there's like a data wall.
I'm not an expert in that domain, but it's been talked about a lot.
And you can read a lot about it.
There's a lot of interesting opportunities, though, to generate data, too.
So there's a lot of people working on simulation.
If you think about a domain like self driving cars, simulation is a really interesting way to generate.
Is that synthetic data?
Is that what? Yeah, I would say that synthetic data.
The synthetic data has a, simulation and synthetic data are a little different, so you can generate synthetic data, like generate a novel.
Simulation, I would put, at least in my head, and I'm sure, like, academics might critique what I'm saying, but I've used, simulation is based on a set of principles like the laws of physics, so if you were to build a real world simulation for training a self -driving car, you're not just generating arbitrary data, like the roads don't turn into loop -de -loops, because that's not possible with physics.
So by constraining a simulation with a set of real -world constraints, the data has more efficacy.
And there's sort of a, it constrains the different permutations of data you can generate from it, so it's, I think, a little bit higher quality.
But then along those lines, a lot of people wonder if you generate synthetic data How much value can that add to a training process?
Is it sort of a regurgitating information already had?
What's really interesting about reasoning and reasoning models is I think, I feel really optimistic these models are generating that new ideas and so it really affords the opportunity to break through some of these, the data wall as well.
So data's one thing and I think both synthetic data and simulation are really interesting opportunities to grow there.
Then you have compute.
And this is something that, you know, that's why there's so many data center investments.
It's why NVIDIA as a company has grown so much. The probably the more interesting kind of breakthroughs there are these reasoning models where there's not quite such a formal separation between the training process and the inference process where you can spin more compute at the time of inference to generate more intelligence which has really been a breakthrough in a variety of ways.
I think it's really interesting but it shows you how you can run up against walls and and find new opportunities to use it and then finally algorithms and the biggest breakthrough was obviously the Transformers model attention's all you need that paper from Google that sort of led to where we are now but there's been a number of really important papers since then from the idea of chain of thought reasoning into what at OpenAI what we did with the 01 model which is to do some reinforcement learning those chains of thought to really reach new levels of intelligence.
And so I do think that I mentioned some anecdotes about some breakthroughs there because my view is that each one of them has their own problems you know compute.
It's very capital intensive and a lot of these models, the half life of their value is pretty short because new ones come out so frequently and so you wonder, can we afford, what's the business case for investing this CapEx and then you have a breakthrough like 01 and you're like gosh, with a distilled model and moving more to inference time, it changes the economics of it.
You have data, you say gosh, for running out of textual data to train on, well, now we can generate reasoning.
We can do simulations.
Oh, that's an interesting breakthrough.
And then, on the algorithm side, as I mentioned, just the idea of these reasoning models is really novel itself.
And each of these at any given point, if you talk to an expert in one of them, and I'm an expert in none of them, they will tell you the sort of current plateau that they can see on the horizon.
And there usually is one.
I mean, you'll talk to different people about how long the scaling laws for something will continue.
and you'll get slightly different opinions, but no one thinks it's gonna last forever.
And at each one of those, because you have so many smart people working on them, you often have people discovering a breakthrough in each of them.
And so as a consequence, I really do feel optimistic about the progress towards HEI because one of those plateaus might extend a while if we just don't have the key idea that we need to break through.
The idea that we will be stuck on all three of those demands feels very unlikely to me and in fact, what we've seen because of the potential economic benefits of AGIs, we're in fact seeing breakthroughs in all three of them and as a consequence, you're just seeing just the blistering pace of progress that we've seen over the past couple years.
At what point does AI start making AI better than we can make it or making it better while we're sleeping or we can't be too far from that?
Well, it might reflect back to our software engineering discussion, but you know, the broadly, this is the area of AGI around self -improvement, which is meaningful from an improvement standpoint, but also obviously from a safety standpoint as well.
So I, um, I don't know when that will happen, but I do think, you know, by some definition, you could argue that it's happening already in the sense that every engineer in Silicon Silicon Valley is already using coding agents and platforms like Cursor to help them code, so it's contributing already.
And I imagine, as coding assistants go to coding agents in the future, most engineers in Silicon Valley will show up in the morning and...
But, this is sort of the difference between, you know, the assisted driving in Tesla versus like, self -driving, right?
Like, at what point do we leap from, I'm a copilot in this, to… I don't have to do anything.
I mean it's a question that there's so much nuance to the answer I'm not sure how to answer because I'm not sure you'd want to necessarily, like I think for some software applications that's important, but when we brought up, we were talking about the active software development, people have to be accountable for the software that they produce.
And that means if you're doing something simple like a software as a service application and that it's secure, that it's reliable, that the functionality works as intended for something as meaningful as an agent that is somewhat autonomous, does it have the appropriate guardrails?
Does it actually do what the operators intended?
Is there appropriate safety measures?
So I'm not sure there's really any system where you'd want to turn a switch and go get your coffee.
But I do think, to the point on you know, these broader safety things is I think that when you think about more advanced models, we need to be developing not only more and more advanced safety measures and safety harnesses, but also using AI to supervise AI and things like that.
So it's a part, probably my colleague on the board, Zico Coulter, is probably a better person to talk through some of the technical things, but there's a lot of prerequisites again at that point, and I'm not sure it's simply like the availability of the technology.
just because it is that, at the end of the day, we are accountable for the safety of the systems we produce.
Not just OpenAI, like every engineer.
And that's a principle that should not change.
What does that mean, like when we say safety and AI?
That seems so vague in general that everybody interprets it quite differently.
Like how do you think about that, and how do you think about that in a world where, let's say we regulate safety in the United States, and another country doesn't regulate safety.
How does that affect the dynamic of it?
I'll answer broadly and then go into the regulatory question.
So, I really like openAI's mission, which is to ensure that AGI benefits all of humanity.
That isn't only about safety, and I believe intentionally so, though obviously the mission was created prior to my arrival.
Because it's both about safety, kind of the first do no harm, and I don't think one could Credibly achieved that mission if we created something unsafe so I would say that's the most important part of the mission But there's also a lot of other aspects of benefit in humanity.
Is it universally accessible?
Is there a digital divide where some people have access to AGI and some don't?
Similarly you could argue that does it are we maximizing the benefits and minimizing the downsides clearly AI will disrupt some job but it also could democratize access to healthcare, education, expertise.
So, as I think about the mission, it starts with safety.
But I actually like thinking about it more broadly because I think at the end of the day, benefitting humanity is the mission and safety is a prerequisite.
It's almost like going to my analogy of the Hippocratic Oath, a doctor's job is to cure you.
First do no harm, but then to cure you.
and a doctor that did no harm but didn't care, you wouldn't be great either.
So I really liked to think about the holistically.
And again, Zika or Sam might have a more complete answer here, but broadly, I think about does the system that represents AGI align with the intentions of the people who created it and the intention of the people operating it, so that it does what we want and it's a tool that benefits humanity, a tool that we're actively using to affect the outcomes that we're looking for.
And that's kinda the way I think about safety.
And it can be meaningful things like misalignment or more subtle things like unintended consequences.
And I think that latter part is probably the area that is really interesting from an intellectual and ethical standpoint as well.
If I look at, what was the bridge in Canada that fell down where it motivated the ring that a lot of engineers...
Oh, yeah, I forget the name of it, but yeah.
Just look at it, whether it's the Tacoma Narrows Bridge in Washington or Three Mile Island or these intersections where we've engineered these, what at the time people hoped would be positively impact humanity, but something went horribly wrong.
Sometimes it's engineering, sometimes it's bureaucracy, sometimes it's a lot of things, And so I don't think when I think about safety, I don't just look at the technical measures of it, but how does this technology manifest in society?
How do we make decisions around it?
And you could take put it another way.
Technology is rarely innately good or bad, it's sort of what we do with it.
Um, and I think those social constructs and a matter a lot as well.
Um, so I think it's a little early to tell cause we don't have this kind of superintelligence right now.
And I think it won't just be a technology company defining how it manifests in society.
And you could imagine taking a very well -aligned AI system and a human operator directing it towards something that would objectively hurt society.
And there's a question of like who gets to decide who's accountable?
And it's a perennial question.
I mean, it's whether you're deciding should you use your smartphone in school?
Who should decide that?
And there's parents who will tell you, hey, it's my decision, it's my kid.
And then there's principals that will tell you it's not benefiting the school.
And I'm not sure that's gonna be my place or our place.
But there'll be a number of those conversations that are much deeper than that question that I think we'll need to answer.
As it relates to regulation, there's two not conflicting forces, but two forces that exist somewhat independently but relate to each other.
One is the pace of progress in AI and ensuring that the folks working on frontier models are ensuring those models do benefit humanity.
And then there's the sort of geopolitical landscape, which is, do you want AGI to be created by the freedom sort of the West, by democracies?
Or do you want it to be created by more totalitarian governments?
And so I think the inherent tension for regulators will be a sense of obligation to ensure that the technology organizations creating EGI are, in fact, focusing enough on that infinity and humanity, all the other stakeholders there whose interests that they're accountable for and ensuring that the West remains competitive.
I think that's a really nuanced thing and I think my view is it's very important that the West leads in AI and I'm very proud of the fact that OpenAI's based here in the United States and we're investing a lot in the United States.
I think that's very important.
And I also, having sort of seen inside of it, I think we're really focused on benefiting humanity.
So I tend to think that it needs to be a multistakeholder dialogue, but I think there's a really big risk that some regulations could have the unintended consequence of slowing down this larger conversation.
But I don't say that to be dismissive of it either.
It's actually just an impossibly hard problem.
And I think you're seeing it play out, as you said, in really different ways in Canada, the United States, Europe, China, elsewhere.
I want to come back to compute and the dollars involved.
So I mean, on one hand you have, if I could start an AI company today by going, putting my credit card down, using AWS and leveraging their infrastructure, which they've spent the hundreds of billions of dollars and I get to use it on a time -based model.
On the other hand, you have people like Open AI and Microsoft investing tons of money into that maybe more proprietary or how do you think about the different models competing and then the one that really throws me for a bit of a loop is Facebook.
So Facebook has spent, Meta, sorry.
You know the mother made a name.
Yeah, oh God. So Meta, I'm like aging myself here.
So Meta comes along and you know, possibly for the good of humanity, but like I tend to think Zuck is like incredibly smart.
So I don't think, I don't think he's spending $100 billion to develop a free model and give it away to society.
How do you think about that in terms of return on capital and return on investment?
It's a really complicated business to be in just given the capex required to build a frontier model.
But let me just start with a couple definitions of terms I think are useful.
I think most large language models I would call foundation models.
And I like the word foundation because I think it will be foundational to most intelligent systems going forward and most people building modern models, particularly if they involve language image or audio, shouldn't start from building a model from scratch. They should pick a foundation model, either use it off the shelf or fine tune it.
And so it's truly foundational in many ways.
In the same way, most people don't build their own servers anymore.
or they lease them from one of the cloud infrastructure providers I think foundation models will be something trained by companies that have a lot of capex and leased by a broad range of customers who have a broad range of use cases.
And I think that leads, and in the same way that data center builders having a lot of data centers enabled you to have the capital scale to build more data centers, I think the same will largely be true of building the huge clusters to do training and things like that foundation models I think are somewhat distinct from frontier models and frontier models I think it's a term credited to Reed Hoffman but I may be mistaken on that that's where I heard it from and these are the models that are usually like the one or two that are clearly the leading edge o3 for example from opening I and these frontier
models are being built by labs who are trying to build AGI that benefits humanity and And I think if you're deciding whether you're building a foundation model and what your business model is around it, it's very different business, then I'm going to go pursue AGI.
Because if you're pursuing AGI, really there's only one answer, which is to build and train and move to the next horizon because if you can truly build something that is AGI, economic value is so great.
I think there's a really clear business case there.
If you're pre -training a foundation model that's the fourth best, that's gonna cost you a lot of money.
And the return on that investment is probably fairly questionable because why use your fourth best large language model versus a frontier model or an open source one from Meta?
And as a consequence of that, I think we probably have too many people building models right now.
There's already been some consolidation actually of companies being folded into Amazon and Microsoft and others, but I do think it will play out a bit like the cloud infrastructure business, where a very small number of companies with very large CapEx budgets are responsible for both building and operating these data centers and then developers and consumers will use things like chatGPT as a consumer or as a developer.
You'll one of these models in the cloud.
How it will play out is a really great question.
I heard one investor talk about these as the fastest depreciating assets of all time.
On the other hand, if you look at the revenue scale of something like an OpenAI and what I've read about places like Anthropic, let alone Microsoft and Amazon, it's pretty incredible as well.
And so you can't really if you're one of those firms, you can't afford to sit on the sidelines as the world transforms. But I would have a hard time personally, like funding a startup that says I'm going to do pre -training.
You know, it's it's I don't really know like what's your what's your differentiation in this marketplace.
And I think a lot of those companies, you're already seeing them consolidate because they have the cost structure of a pharmaceutical company, but not the business model.
This is just it though right like OpenAI has a revenue model around a revenue model.
Microsoft has a revenue model around their AI investments.
They just updated the price of teams with copilot.
Amazon has a revenue model around AI in the sense that getting other people to pay for it through AWS and then they're getting the advantages of it at Amazon too from a consumer point of view and all the millions of projects Bezos was doing an interview last week he said there's every project at Amazon basically has an AI component to it now Facebook on the other hand has spent all of this money already and with you know an endless amount presumably Insight or like not in sight an endless amount to go But they don't have a revenue model specifically around AI where it would have been cheaper obviously for them
to use a different model But that would have required presumably giving data away?
I'm just trying to work through it from Zuck's point of view.
I actually will take Marc at his word. That post he wrote about open source I think was very well written and encouraged people to read it.
I think that's a strategy.
If you look at Facebook, the company has always embraced open source and if I look at really popular things from react to, now the llama models, it's always been a big part of their strategy to court developers around sort of their ecosystem, and Marc articulated some of the strategy there, and I'm sure there's elements of commoditizing your complement, but I also think that if you can attract developers towards models, there's a strength.
I'm not really on the inside there, so I don't really have a perspective on it, other than and I actually think it's really great that there's different players with different incentives all investing so much, and I think it is really furthering the cause of bringing these amazing tools to society.
But a lot changes. I mean, if you look at the price of GPT 4 .0 Mini, it is so much higher quality than the highest quality model two years ago, and much cheaper.
I haven't done the math on it, but it's probably cheaper to use that than to self -host any of the open source models.
So even the existence of the open source models, it's not free.
I mean, inference costs money.
So there's a lot of complexity here and actually, I have the e -mail even being relatively close to stuff, like, I have no idea where things are going.
But you could talk to a smart engineer and they'll tell you, oh yeah, have you built your own servers you'll spend less than renting them from say Amazon Web Services or Azure.
That's sort of true in absolute terms, but misses the fact like do you want someone in your team building servers?
Oh, and in fact, if you change the way your service works and you need a different SKU, like you all of a sudden are doing training and you need Nvidia H100s, now all of a sudden you're built servers like this asset that's worthless.
So I think with a lot of these models, you know, the presence of open source is incredibly important and I really appreciate it.
I also think, like the economics of AR are pretty complex because the hardware is very unique.
The cost to serve is much higher.
Techniques like distillation have really changed the economics of models, whether or not it's open source or hosted and leased.
So it's, I think broadly speaking for developers it's kind of an amazing time right now because you have a menu of options that's incredibly wide and I actually think of it as, you know, just like in cloud computing you'll end up with a price performance quality trade off and for any given engineering talents they'll have a different answer and that's appropriate.
And some people use open source Kafka, some people work with Confluent.
Great. You know, like, that's just the way these things work, you know?
And, uh... So you don't think AGI is going to be like a winner -take -all?
You think there's going to be multiple options that have, by definition, whatever the definition is of AGI?
Well, first, I think open AI, I believe, will play a huge part in it, because there's both the technology, which I think open AI continues to lead on, but also chat TPT, which has become synonymous with AI for most consumers.
but more than that, it is the way most people access AI today, and so, one of the interesting things, like what is AGI?
We talked about opinions on what the definition might be, but the other question was like, how do you use that?
Like, what is the packaging?
And some of intelligence will be simply the outcomes of it, like a discovery of a new drug, which would be remarkable, and hopefully we can cure some illnesses, But others will be just how you as an individual access it, and most of the people I know, if they're signing an apartment lease, we'll put it in the ChatGPT, legal opinion.
If you get lab results from your doctor, you can get a second opinion on ChatGPT.
Clay and I use the 01Pro mode for criticizing our strategy at CIRA all the time.
And so for me, what's so remarkable about ChatGBT, which was this, you know, quirkily named research preview that has come to be synonymous with AI as I do think that it will be the delivery mechanism for AGI when it's produced and not just because of the many researchers at OpenAI but because of the amazing like, utility it's become from individuals.
I think that's really neat because I don't know if it would have been obvious if we were having this conversation three years ago, you know in your talking about artificial general intelligence I'm not sure either of us would have envisioned something so simple as a form factor to absorb it that you just talk to it.
So I think it's great and especially as I think about the mission of OpenAI which is to ensure that AGI benefits humanity.
What a simple accessible form factor.
There's free tiers of it like what a kick -ass way to benefit humanity so I really think That will be central to what we come as society to define as AAGI.
You mentioned using it at Sierra to critique your business strategy.
What do you know about prompting that other people miss?
I mean, you must have the best prompts.
People think that, you know, because I'm affiliated with it.
You're not going like, here's my strategy, what do you think?
What are you putting in there?
I often, with the reasoning models, Which are slower, will use a faster model first gbt four zero to refine my prompts.
Um, so, uh, over the holidays, um, partly because I was thinking about the future software engineering, I've, I've written a lot of compilers in my time.
I'm like written enough that I, you know, it's like a, uh, it's, it's easy for me.
So I decided to see if I could have a one pro mode, um, generate in the, and a compiler front -end, parsing the grammar, checking for semantic correctness, generating an intermediate representation, and then using LLVM, which is sort of a compiler collection that's very popular, to actually do, run it all.
And I would spend a lot of time iterating on 4 .0 to refine and make more complete and specific what I was looking for, and then I would put it into a one promo go get my coffee and you know come back and get it.
I'm not sure if that's a viable technique but it's really interesting because I do think in the spirit of AI being the solution to more problems in AI, having a lower latency, simpler model help refine essentially I like to think of it as like you're like a product manager and you're asking you know an engineer what to do is your product requirements document complete and specific enough and waiting for it is sometimes slower, and so I like doing it in stages like that, so that's my trip.
At some point, there's probably someone from OpenAI listening who's gonna roll their eyes, but that's just, that's eyes.
Who can I talk to at OpenAI that's like the prompt intro?
Yeah. I'm like so curious about this because I've actually taken recently to getting OpenAI, or ChatGBT I guess if you wanna call it that, I've been getting ChatGBT to write the prompt for me.
And so I'll prompt it with, I'm prompting an AI.
Here are the key things I want to accomplish.
What would an excellent prompt look like?
And then I'll copy paste that prompt that it gives me back into the system, but I'm like, I wonder what I'm missing here, right?
It's a good technique, I mean, there's lots of new techniques like that.
Like self -reflection is a technique where you have a model, observe and critique, a decision like a chain of thought.
So in general, you know, that mechanism of self -reflection is, I think, a really effective technique.
You know, at CIRA we help companies build customer -facing AI agents, so if you're setting up a Sonos speaker, you'll now chat with an AI, if you're a Sirius XM subscriber, you can chat with Harmony who's their AI manager account.
We use all of these tricks, you know, self -reflection to detect things like hallucination or decision making, generating chains of thought for more complex tasks to ensure that it's, you know, you're putting as much compute and cognitive load into an important trick.
So you know, we're the, there's a whole industry around sort of figuring out how do you exact the like robustness and, and, um, precision out of these models.
So it's really fun, but changing rapidly.
Hypothetical question.
And you you've been hired to lead or advise a country that wants to become an AI superpower.
What sort of steps would you take?
What sort of policies would you think would help create that?
How would you bring investment from all over the world into that country and researchers?
Right. So now all of a sudden you're competing.
It's not the United States.
Like how do you how do you sort of set up a country like from first principles all the way back to like what does that look like?
What are the key variables?
Well I mean especially, this is definitely outside of my domain of expertise, but I would say one of the key ingredients to modern AI is compute, which is a noun that wasn't a noun until recently, but now compute is a noun.
I do think that's one area where policy makers can, because it involves a lot of things that touch federal and local governments like power, land.
And then similarly attracting the capital, which is immense to finance to the real estate, to purchase the compute itself, and then to sort of operate the data center.
And again, there's really immense power requirements for these data centers as well.
And then it's attracting sort of the right researchers and research labs to leverage that.
But in general, where there is compute, the research labs will find you.
So I think that's it.
And then there's a lot of national security implications too just because these models are very sensitive, at least the frontier models are.
And so your place in the geopolitical landscape is quite important.
Like will research labs and will the U .S. government be comfortable with training happening there and export restrictions and things like that.
But I think a lot of it comes down to infrastructure as it relates to policy is my intuition.
I think right now so much of AI is constrained on infrastructure that that is the input to a lot of this stuff.
And then there's a lot around attracting talent and all that, but as I said, you look at the research labs, it's not that many people actually, it's a lot but their kind of compute is the limited resource right now.
That's a really good way to think about it.
I think about this through the lens of Canada, right?
Which is like we don't have enough going on in AI.
We tend to lose most of our great people to the States, who then go to set up infrastructure here for whatever reason, and don't bring it back to Canada.
And I wonder how Canada can compete better.
So this is like sort of the lens.
I like look at these questions through.
How do you see the next generation of education?
Like, if you were setting up a school today from scratch, and again, hypothetical, not your domain of expertise, but like using your lens on AI, how do you think about this?
So like what skills will kids need in the future, and what skills do we – probably don't need to teach them anymore, that we have been teaching them?
Well I'll start with the benefits that I think are probably obvious but I'm incredibly excited about, I think education can become much more personalized.
Oh, totally. Have you seen Synthesis Tutor by the way?
No, I have not. Oh, so they developed this, uh, Synthesis, this AI company, developed this tutor which actually teaches kids and it's so good that El Salvador the country just recently adopted and replaced their teachers, and, uh, like it'll teach you but it teaches you specific to what you're missing.
So it's not like every lesson's the same, it's like, well you're not understanding this foundational concept, so it's like K through five or six right now.
That's amazing, and you know I actually, And the results are off the charts.
Well, it doesn't surprise me, and I don't actually view it as necessarily replacing a teacher, but my view is if you have a teacher with 28 kids in his or her class, the likelihood that they all learn the same way, or learn at the same pace, is very unlikely.
And you know, I can really think of a, say, an English teacher, a history teacher, or orchestrating their learning journeys through a topic, say AP European history in the United States.
There's a curriculum.
They need to learn it.
How someone will remember something or understand the significance of Martin Luther, you know, is very different.
And you can, you know, generate an audio podcast for someone who might be an auditorial learner.
You can create cue cards for someone who needs that kind of repetition.
You can visualize key moments in history for people who just maybe want to more viscerally appreciate why this was a meaningful event rather than this dry piece of history.
And all of that, as you said, could be personalized to the way you learn and how you learn, I think is just incredibly powerful.
And so one of the things I think is neat about AI is it's democratizing access to a lot of things that used to be fairly exclusive.
A lot of wealthy people, if their child was having trouble in school, would pay for a tutor, a math tutor or science tutor.
And you know, if you look at kids who are trying to get into big name colleges, if you have the means, you'll have someone prep you for the SATs or help you with your college essays, all of that should be democratized if we're doing our jobs well.
and it means that we're not limiting people's opportunity by their means, and I think that's just the most American thing ever, Canadian as well.
It's the most incredible thing for humanity.
It's the most incredible thing, humanity, and so I just think education will change for the positive in so many ways, because I actually, with my kids walking around, when they ask, you know, you have little kids, they ask, why, why, why, you know, there's some point a parent just starts making up the answer of being dismissive, and like, we have ChatGPT out, and it's like the best when you're traveling, and...
Put on advanced voice mode, and be like, ask away!
100%, and I'm listening, too, you know, it's like you're, you live through your children's curiosity and my daughter went to high school and came home with Shakespeare for the first time and I was, she asked me a question, I was like, I felt this is like total inadequacy, I was like, I was very bad at this the first time and then we put it into ChatGPT and it was the most thoughtful answer, and she could ask follow -up questions.
And I actually was with her because I was like, oh, I forgot about that.
Didn't even think about that.
So I just think it's incredible.
And I would like to, in public school systems, I think it's really, I think it will be a really great when public school systems formally adopt these things so that they lean into tools like ChatTPT to TPT as mechanisms to raise the performance level of their classroom.
And, hopefully, you'll see it in things like test scores and other things, because kids can get the extra time.
Even if the school system can't afford it for everyone.
And then, most importantly, kids are getting explanations according to their style of learning, which I think will be quite important as well.
As it relates to skills, it's really hard to predict right now.
and I would say that I do think learning how to learn and learning how to think will continue to be important.
So I think most of primary and secondary education shouldn't and is not vocational necessarily, some of it is.
I took auto shop and all of that and I'm glad I did, but I couldn't fix my electric car today with that knowledge, things change.
And I don't think it needs to be purely non -vocational, but the basics of learning how to think, learning, writing, reading, math, physics, chemistry, biology, not because you need to memorize it, but understand the mechanisms that create the world that we live in is quite important.
I do think that there's a risk of people sort of becoming ossified in the tools that they use.
Um, so, you know, uh, uh, let's go back to our discussion of software engineering for a second, but I'll give other examples.
You know, if you define your role as a software engineers, how quickly you type into your IDE, the next few years might leave you behind, you know, because that, um, that is no longer a differentiated, you know, part of the software engineering experience or will not be, but your judgment as a software engineer will continue to be, uh, incredibly important.
in your agency and making a decision about what to build, how to build it, how to architect it, maybe using AI models as a creative foil.
And so I think that just in the same way, if you're an accountant, you know, using Excel doesn't make you less of an accountant, and just because you didn't, you know, hand craft to that math equation, and it doesn't make the results any less valuable to your clients.
And so I think we're going to go through this transformation where I think the tools that we use to create value in the world will change dramatically.
And I think some people who define their jobs by their ability to use the last generation's tools really really effectively will be disrupted.
But I think if we can empower people and to reskill and also broaden the aperture by which they define the value they're providing into the world, I think a lot of people can make the transition.
The thing that is sort of uncomfortable, not really an education, where it's just earlier in most people's lives, is just I think the pace of change exceeds that of most technology transitions, and I think it's unreasonable to expect most people to change their way of work that quickly.
And so I think the next five years I think will be, for some jobs, really disruptive and tumultuous.
process, but if you take the longer view and you fast forward 25 or 50 years, I'm incredibly optimistic.
I think the change will require, from society, from companies, and from individuals, an open -mindedness about reskilling and reimagining their job to the lens of this dramatically different new technology.
At what point do we get to ...
I mean, we're probably on the cusp of it now, and it's happening in pockets.
But what point do we start solving problems that humans haven't been able to solve or eliminating paths that we're on maybe with medical research that it's like, no, that that this whole thing you've spent $30 billion on, you know, based on this 1972 study that was fabricated.
But that one study had all these derivative studies and like, I'm telling you, it's false, you know, because I can look at it through an objective lens and get rid of these 30 billion while you're smiling.
Oh no, I just, I hope soon.
I mean I hope, I mean I, there was a lot of, there's one of the models I can't remember which one introduced a very long context window and there was a lot of people on exch over the weekend putting in their thesis, right, grad school thesis in there, and it was actually critiquing them with like surprising levels of fidelity.
And I think we're sort of there perhaps with the right tools, but certainly over the next few years.
We talked about what does it mean to generalize AI.
Certainly in the areas of science that are largely represented through text and digital technology, like math being probably the most applicable, there's not really anything keeping AI from getting really good at math, there's not really an interface to the real world, you don't need to do a clinical trial to verify something's correct.
So I feel a ton of optimism there.
It'll be really interesting and like, you know, areas of like theoretical physics, you'll see, you'll continue to have the divide between the applied and the theoretical people.
But I think there could be like really interesting new ideas there.
And perhaps some finding logical inconsistencies with some of the, you know, fashionable theories were just happened many times over the past few decades.
I think I think we'll get there soon.
And actually what's really neat about it is most of the scientists, I know people who are actually like doing science, they're the most excited about these technologies.
And they're using them already and I think that's really neat, I think we're hopefully going to be...
I really hope we see more breakthroughs in science.
One of the things I am not an expert in but I've read a lot like a lot as an amateur about is just the slow down in scientific breakthroughs over the past few decades and some theories that it's because of the degree of specialization that we demand of grad students and things like that.
And I hope in general with AI democratizing access to expertise, I have a completely personal theory that it will benefit deep generalists in a lot of ways too because your ability to understand a fair amount and a lot of domains and leveraging AI, knowing where to prompt the AI to go explore and bringing together those domains.
It will start to shift the intellectual power from people who are extremely deep to people who actually can orchestrate intelligence between lots of different domains for breakthroughs.
I think they'll be really good for society, because most scientific breakthroughs aren't.
They tend to be cross pollinating very important ideas from a lot of different domains, which I think will be really exciting.
How important is the context window?
I think it could be quite important.
Especially it certainly simplifies working with an AI.
You could just give it everything and instruct it to do something.
And so assuming it works, you can extend the context window and it can, the attention could be spread fairly thin and the robustness of the answer can be questionable.
But assuming, and let's just for argument's sake, perfect robustness, I think it can really simplify the interface to AI.
Not all uses, I also think that we were talking about open -source models and APIs.
I also think that most, what I'm excited about in the software industry is not necessarily a large language model with a prompt and a response being the product of AI, but actually end -to -end closed loop systems that use large language models as pieces of infrastructure, and I actually think that a lot of the value in software will be that, and for many of those applications the context window size can matter, but often because you have contextual awareness of the process that you're executing, your context window is a little bit less important.
I think it matters a lot to intelligence.
You know, there's a, I can't remember, some researcher said, you put all of human knowledge in the context, Wendy, and you ask it to invent the next thing, and that's, obviously, a reductive thought, but interesting.
But I actually, I'm equally excited about sort of the industrial applications of large language models, sort of like my company, Sierra, and if you're returning a pair of shoes at a retailer, and it's a process that's fairly complicated and, you know, is it within the return window?
So do you wanna return it in store or do you wanna send it?
Do you wanna print a QR code, blah, blah, blah, blah?
The orchestration of that is as significant as the models themselves.
And I actually think, just like computers, there's gonna be a lot of things where computers are a part of the experience but it's not like manifesting itself as a computer.
So I'm actually equally excited about those and I think context window is slightly less important than those applications.
Do you think that the output from AI should be copyrightable or patentable?
Let me just take an example.
If I go to the US patent office, I download a patent for let's say the AeroPress and I uploaded to 01 Pro and I say, I can't upload it yet because you don't let me do the PDFs, but I put it to four and so I say, Hey, what's the next logical leap that I could patent off this?
It would give me back diagrams and an output and presumably, if I look at that and I'm like, yeah, that's legit, I want to file that patent.
Can I, I don't know how to answer the question.
I'm not an expert in sort of intellectual property, but I, uh, uh, I think there'll be an interesting question of, was that your idea?
Because you used a tool to do it.
I think the answer is probably yes that you, you used the tool to do it.
But I also think that the, in general, like the sort of marginal cost of intelligence will go down a lot.
So a lot of the, you know, I think in general, like we'll be in this renaissance of new ideas and intelligence being produced.
And so I think that's broadly a good thing and I think, you know, the marginal value of that insight that you had might be lower than it was n years ago.
What I was hoping you would say is that you know that's gonna become less and less Important because I feel like all the patent trolls and all of this stuff that slows down innovation in some ways Obviously like there's legitimate patents that people infringe on and there should be legal recourse But if I could just go and patent like a hundred things a day Cuz I'm it seems like that should not be allowed. This is what I'm saying though general I think that you know companies I think Patents make sense if it's projecting something that's an active use that you invented and you're trying to, like
the standard legal rationale for patents.
Just generating a bunch of ideas and patenting that seems destructive to the value of a company.
So here's the idea I had last night to counter this because I was like, I don't want somebody doing this.
And I was thinking, prior art eliminates patents.
So I was like, what if I just set up an instance and just publish it on a website?
Nobody has to read that website.
Here's a billion idea.
Exactly but it's like basically patenting like anything.
Priorart .org But it's creating prior art for everything.
So like you can't compete on that anymore.
I don't know. I was like thinking about that.
I thought it was fun.
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Tell me about the Google map story.
This is like now legend.
And I want to hear it from you.
This is my weekend coding.
Is that what you want to hear about?
Yeah. Yeah. So I'll start with just like the story of Google Maps, the abbreviated version.
We had launched a product at Google called Google Local, which was sort of a Yellow Pages search engine.
You probably most listeners don't even know what Yellow Pages are, but it was a thing back then, and we had licensed Maps from MapQuest, which was a dominance mapping provider at the time and it was sort of an eyesore on the experience and also always felt like it could be a more meaningful part of the kind of local search and navigation experience on Google.
So Larry Page in particular was really pushing us to really invest more in Maps.
We found this small company with like four people in it, if I'm remembering correctly, started by Lars and Jens Rasmussen called Where's Hue Technologies where they had made a Windows application application called expedition that was just a beautiful mapping product.
It was running on windows long after it was sort of out of fashion to make windows apps, but they were sort of, where the technology they're comfortable with.
But they're really, their maps modelled the A to Z maps in the UK and were just beautiful and they just had a lot of passion for mapping and so we did a little aquahera of them and took together the Google local team and Lars and Jens's team and and said, okay like, let's take the good ideas from this Windows app and the good ideas from Google local and like let's bring them together to make something completely new.
And that's what became Google Maps.
But there's a couple of idiosyncrasies in the integration because it was a Windows app, it really helped and hurt us in a number of ways.
Like one of the ways it helped us is the reason why Google Maps, we were able to drag the map and it was so much more interactive than any web application that preceded it was the standard that we needed to hit from interactivity was set by a native Windows app, not set by the legacy websites that we had used at the time and I think that by having the goalposts so far down the field because they had just started with this Windows app which was sort of a quirk of Lars and Jens It's just like technical choices.
We made much bolder technical bets than we would have otherwise.
I think we would have ended up much less interactive had we not started with that quirky technical sort of decision.
But the other thing was this Windows app, there was a lot of like, it's hard to describe the like early 2000s, people didn't live it, but like XML was like really in fashion.
So like most things and windows and other places was like XML and XSLT, which was a way of transforming XML into different XML was the basis of everything.
It was like all of enterprise software, it was like XML this, XML that.
So similarly, when we were taking some of these ideas and putting them in a web browser, we kind of like went into autopilot and used like a ton of XML, and it made everything just like really, really tedious.
And so Google Maps launched with some really great ideas, like the draggable maps, and And we did a bunch of stuff for the local search technology so you could overlay restaurant listings, it was really great.
It was a really successful launch. We were like the hot shots within Google afterwards.
But it really started to show its craft and we got to this point where we decided we wanted to support the Safari web browser which was relatively new at the time, this was before mobile phones and there was much less XML support in Safari than there was in Internet Explorer and Firefox.
And so one of the engineers implemented like a full XSLT transform engine in JavaScript to get it to work and it was just like shit on top of shit on top of shit.
And so what was a really elegant like fast web application had sort of quickly become something, you know, there's a lot of dial -up modems at the time.
And so like you'd show up to maps and it just was slow and like it just bothered me as like someone who takes a lot of pride in their craft. And so I got really energized over a weekend and a lot of coffee, like rewrote it.
But it was - Rerewrote the whole thing, though.
Rerote, yeah, more or less the whole thing.
And it took probably another week of working through the bugs.
But yeah, I sent it out to the team after that weekend.
And the reason I was able to do it, yeah, I'm like a decent programmer.
But you had also lived with every bad decision up to that point, too.
So I knew exactly the output I was gonna, like I had simulated in my head like if I could do it over again this is the way I do it.
So by the time I like put my hands on the keyboard on like Friday night, it wasn't like I was designing a product.
Like I knew I had been in every detail of that product since the beginning including made the bad decisions too, the not all the bad decisions.
And so it was just very clear.
I knew what I wanted to accomplish and for any engineers who worked on a big system you have the whole system mapped out in your head.
So I knew everything.
And I also knew that there's a lot of pride of authorship with engineering and code.
So I sort of knew I really wanted to finish it over the weekend so that people could use it and see how fast it was and kind of overcome anyone who was like, you know, protective of the code they had written a few months ago.
And so I really wanted the prototype to go out.
So I did it. And then, I didn't, it's funny, I never talked about again, but I think Paul Buhay, who was a co -creator of the Gmail, and I worked and started FriendFeed with me, he was on an interview and mentioned the story, so now all of a sudden it's like everyone's talking about it, and I was like, well thank you Paul, I'm a little embarrassed that people know about it, but it's a true story, and XML is just the worst. And so.
Did you get a lot of flack from the people who had built the system you effectively replaced?
Like you were part of that team, but everybody else had so much invested in it, even though it was like shit on top of shit on top of shit.
You know, I wrote a lot of it, too.
So, yeah, I'm sure there was some around it.
But actually, I think good teams wanna do great work.
And so I think there was a lot of people constructively dissatisfied with the state of things, too.
And I think, you know, the engineer had written that XSLT transform, I think was like, you know, a little bit, that's a lot of work.
So you have to throw out a lot of work, which feels bad.
But particularly, you know, um, Lars and Jens and I like we want to make great products.
And so I don't think there was a, yeah, at the end of the day everyone's like, wow, that's great.
You know, we went from a bundle size of 200 K to a bundle size of 20 K and it was a lot faster and better.
So, you know, broadly speaking, I think good engineering cultures, you don't want a culture of, um, you know, ready fire aim.
But I also think you just need to be really outcomes oriented.
And I think people if they become, they'd start to treat their code as too precious, it can really impede forward progress.
And I'll just take like I, my understanding is like a lot of the early self driving car software was a lot of hand coded heuristics and rules.
And, you know, a lot of smart people think that eventually it will probably be a more monolithic model that encodes many of the same rules.
you have to throw out a lot of code in that transition, but it doesn't mean it's not the right thing to do.
And so I think in general yeah, there might have been some feathers ruffled, but at the end of the day everyone's like that's faster and better, like let's do it.
Which is, I think, the right decision.
That's awesome. And to give you another hypothetical, I want you to share your inner monologue with me as you think through it.
So if I told you you have to put 100 % of your net worth into a public company today, and you couldn't touch it for at least 20 years, What company would you invest in?
And walk me through your thinking.
I literally don't know how to answer that question.
How would you think about it without giving me an answer?
Yeah that's good question.
First of all, give you how I'd think about it, but I'm so, having not been a public company CEO for a couple years, I'm blissfully don't pay attention as much to the public markets, and in particular right now it's obviously valuations have gone up a lot.
So there's a, but because it's a long -term question or maybe that doesn't matter.
I think what I'd be thinking about right now is over the next 20 years, what are the parts of the economy that will most benefit from this current wave of AI?
That's not the only way to invest over a 20 year period but certainly it's a domain that I understand and in particular, I mentioned that talk I heard a snippet of from Tyler Cohen which is AI will probably benefit at different parts of the economy disproportionately, there will be some parts of the economy that can essentially, where intelligence is a limiting factor to its growth and where you can absorb almost arbitrary levels of intelligence and generate almost arbitrary levels of growth.
Obviously there's limits to all of this just because you change one part of the economy, it impacts other parts of the economy and that was what Tyler's point was in his talk.
but I would probably think about that because I think that over a 20 year period, there are certain parts of society that won't be able to change extremely rapidly, but there will be some parts that probably will and it'll probably be domains where intelligence is the scarce resource right now.
And then I would probably try to find companies that will disproportionately benefit from it.
And I assume this is why like Nvidia's stock is so high right now because if you wanna sort of get downstream, you know, and video will probably benefit from all of the investments in AI.
I'm not sure I would do that over a 20 year period, just assuming that the infrastructure will shift. So I don't have an intelligent answer.
But that's the way I would think about it if we're doing that exercise.
I love that. Where do you think like what's your intuition say about what areas of the economy are limited by intelligence and not just economy?
I mean, perhaps politicians might be limited by this and aid and benefit from, in which case countries could benefit enormously from AI and unlock growth and potential in their economy, but I think, maybe, just to scope the question, what areas of the economy do you think are limited by intelligence?
Or workers, like smart workers, in which case, that's another limit of intelligence.
Yeah, I mean, two that are, I think, probably going to benefit a lot are technology and finance.
You know, where you're, you know, if you can make better financial decisions than competitors you'll generate outsized returns, and that's why over the past, you know, 30 years, you know, of machine learning, you know, hedge funds and financial services institutions everything from fraud prevention to true investment strategies it's already been an area of domain of investment software.
Similar as we talked about, I think that at some point we will be, we will no longer be supply constrained in software, but we're not anywhere close to it right now.
And you're taking something that has always been the scarce resource, which is software engineers, and you're making it not scarce.
And I think as a consequence, if you just think of like how much can that industry grow?
We don't know, But we've been so constrained on software engineering as a resource Who knows over the next 20 years, but we'll find out where the limits are but to me intellectually There's just a ton of growth there And then broadly I think areas of like processing information are areas that will Really benefit quite a bit here And so that and I think the the thing that I would think about over 20 year period is like second and third order effects which is why I don't have an intelligent answer.
And if you're asking me to put all my money in something, I would think about it for a while, probably use 01Pro a little bit to help me.
But, you know, because you can end up generating a bunch of growth in the short term, but then, if everyone does it, it commoditizes the whole industry type of thing.
So, there used to be before the introduction of the freezer, ice was like a really expensive thing and now it's free, and so I think it is really important to actually think through those if you're talking a timeframe of like 20 years.
And that's why having not thought about this question ahead of time, I, um, you could be quite simplistic elsewhere, but I would say software and finance or is that I, I think standard reasons should benefit quite a bit.
I love that response.
How do you balance, uh, having a young family with also running a startup again?
I work a lot. Um, I don't, uh, I really care and love, care about and love working.
So, one thing is that I, well, there's always trade -offs in life.
If I didn't love working, I wouldn't do it as much as I do, but I just love, love to create things and love to have an impact.
And so, I like jump out of bed in the morning and work out, go to work, and then spend time with my family, broadly probably, you know, being honest. First I'm not perfect at it, but second, I don't have a ton of hobbies, you know, basically work and spend time with my family.
The first time we talked you saw the couple guitars in my background yeah I haven't picked one of those up in a while yeah I mean I literally pick it up occasionally but I you know do not devote any time into it and I don't regret that either like I am so passionate about what we're building at Cira I'm so passionate about opening I am so love my family so much I don't really have any regrets about it but I basically just like life is all about where do you spend your time and mine is that work and with family, and so that's how I do it.
I don't know if I'm particularly balanced, but I don't strive to be either.
I really take a lot of pride and I love to work.
Having sold the companies you started twice, does that influence what you think of Sierra, like are you thinking like, oh, I'm building this in order to sell it or do you think differently, like this is my life's work, I'm building this with, that's not going to happen?
And I absolutely intend Xero to be an enduring company and an independent company.
But to be honest, every entrepreneur with every company starts that way and so, you know, I'm really grateful for both Facebook and Salesforce for having acquired my previous companies and hopefully I had an impact about those companies, but you don't start off, well at least I never started off saying hey, I wanna make a company to sell it.
And, but I actually think with Sierra, we have just a ton of traction in the marketplace.
I really do think Sierra's the leader in helping consumer brands build customer -facing AI agents and I'm really proud of that, so I really see a path to that and I joke with Clay, I want to be an old man sitting on his porch, complaining how the next generation of leaders at Sierra don't listen to us anymore.
I want this to be something that not only is enduring, but outlives me.
And I think just actually, I don't think we've ever talked about this, but it was a really interesting moment for me when Google went from its one building in Mountain View to its first corporate campus, it we moved into the Silicon Graphics campus, which was right over near Shoreline Boulevard and in Mountain View.
And SGI had been a really successful company enough to build the campus.
And when we would it's actually quite awkward, we moved into like half the campus, they were still in half and they're like we're this up and coming company they're declining, and then when Facebook we moved out of the second building we were in Palo Alto with slightly larger build I think we leased it from HP, but we finally got a campus it was from Sun Microsystems who had gone through an Oracle acquisition and had been sort of on the decline and it was interesting to me because both SGI and Sun had been started and grown to prominence in my lifetime.
Obviously I was maybe like a little younger obviously but in my lifetime enough to build a whole corporate campus and then decline fast enough to sell that corporate campus to a new software company.
And for me it was just so interesting to have done that twice to move into like a you know used campus for the previous owners it was a very stark reminder that that technology companies aren't entitled to their future success.
And I think we'll see this actually now with AI.
AI, I think, will change the landscape of software to be tools of productivity that two agents that actually accomplish tasks.
And I think it will help some companies for whom that amplifies their existing value proposition and it will really hurt others where it will essentially the seat -based model of legacy software or will wane very quickly and then really harm them.
And so when I think about what it means to build a company that's enduring, that is a really really tall task in my mind right now because it means not only making something that's financially enduring over the next 10 years, but setting up a culture where a company can actually evolve to meet the changing demands of society and technology when it's changing at a pace that is like unprecedented in history.
So I think it's one of the most fun business challenges of all time.
And I think it has as much to do with culture as it has to do with technology, because every line of code in Sierra today will be completely different.
You know, probably five years from now, let alone 30 years from now.
And I think that's really exciting.
And so when I think about it, I just get so much energy because it's incredibly hard and it's harder now than it's ever been to do something that lasts beyond you.
But that I think is the ultimate measure of a company.
You mentioned AI agents.
How would you define that?
What's an agent? I'll define it more broadly and then I'll tell you how we think about it at CIRA, which is a more narrow view of it.
The word agent comes from agency and I think it means affording a software the opportunity to reason and make decisions autonomously.
And I think that's really all it means to me and I think there's lots of different applications of it.
The three categories that I think are meaningful, and I'll end with the CRO one just so I can talk about it a little more.
But one is personal agents.
So, I do think that most people will have probably one but maybe a couple AI agents that they use on a daily basis that are essentially amplifying themselves as an individual.
You can do the road things, like help you triage your email to help you new schedule a vacation.
You know, you're flying back to Edmonton and help you arrange your travel.
Two more complex things like, you know, I'm gonna go ask my boss for promotion, like help me role -play and, you know, I'm setting up my resume for this job, help me do that too.
I'm applied for a new job.
Help me find companies I haven't thought of that I should be applying to.
And I think these agents will be really powerful.
I think it might be a really hard product to build because when you think about all the different services and people you interact with everyday, it's kind of everything.
So it's not, it has to generalize a lot to be useful to you and because of the personal privacy and things like that, it has to work really well for you to trust us.
I think it's gonna take a while to go.
I think there'll be a lot of demos.
I think it'll take a while to be robust. The second category of agent is I would say really filling a persona within a company.
So a coding agent, a paralegal agent, an analyst agent.
I think these already exist. I mentioned Cursor, there's a company called Harvey that makes a legal agent.
I'm sure there's a bunch in the analyst space.
These do a job and they're more narrow.
But they're really commercially valuable because most companies hire people or consultants that do those things already, like analyze the contracts of the supply chain.
That's a kind of a rote kind of law, but it's really important, and AI can do it really well.
So I think that's why this is the area of the economy that I think is really exciting, and I'm really excited about all the startups in this space because you're essentially taking in what used to be a combination of people and software and really making something that solves a problem.
And by narrowing the domain of autonomy, you can have more robust guardrails and even with current models actually achieve something that's effective enough to be commercially viable today.
And by the way, it changes the total addressable market of these models too.
I don't know what the total addressable market of legal software was three years ago, but it couldn't have been that big.
I couldn't tell you like a legal software company.
I probably should. I just can't think of one.
But if you think about the money we spend on lawyers, that's a lot.
And so you end up where you're broadening the addressable market quite a lot.
The domain we're in, I think is somewhat special which is a company's branded customer facing agent.
And the reason why I think it's, one could argue we're sort of helping with customer service which is a persona a role but I do think it's broader than that because if you think about a website you know like your insurance company's website try to list all the things you can do on it you can look up the stock quote you can look up the management team you can compare their insurance company to all their competitors you can file a claim you can you know buy you can bundle your home an auto you can add a member of your family to your premium there's a million things you can do on essentially over
the past 30 years websites a company's website singular has come to be the universe of everything that you can do with that company I like to think is like the digital instantiation of the company and that's we're helping our customers do it serious help them build a conversational AI that does all of that so you know most of our customers start with customer service and it's a great application because no one likes to wait on hold and and having something that has perfect access to information is multilingual and empathetic is just amazing but you know when you put a conversational AI as your
digital front door people will say anything they want to it and we're now doing product discovery considered purchases going back to insurance example hey you know I've got a 15 year old daughter I really am concerned about the cost of her premium until she grows up, tell me which plan I should be on, tell me why you'll be better than your competitors, that's a really complex interaction, right?
That's not something that, can you make a webpage that does that?
No, but that's a great conversation, and so we really aspire that when you encounter a branded agent in the wild, we want Sierra to be the platform that powers it.
And it's super important because there is a case, at least in Canada, where an AI agent for Air Canada hallucinated a bereavement policy.
But they were found liable to hold themselves to what the agent said.
Yeah, I mean it turns out - And it was an AI agent, there was no human involved in the whole thing.
Well, look, it's one thing if chat GPT hallucinates something about your brand, it's another if your AI agent hallucinates something about your brand, so the bar just gets higher.
So the robustness of these agents, the guard rails, everything is more important when it's yours and it has your brand on it.
And so it's harder, but I also, I'm just so excited for it because this is a little overly intellectual, but I really like the framing.
If you think about a modern website or mobile app, it's essentially, you've created a directory of functionality from which you can choose.
But the main person with agency in that is the creator of the website.
What are the universe of options that you can do?
So when you have an AI agent representing your brand, the agency goes to the customer, they can express their problem any way they want in a multifaceted way.
And so it means that your customer experience goes from the enumerated set of functionality you've decided to put on your website to whatever your customers ask, and then you can decide how to fulfill those requests or whether you want to.
But I think it would really change the dynamic to be really empowering to consumers as you said I mean the reason that that Air Canada case is the reason we exist you know companies if they try to build this themselves there's a lot of new ways you can shoot yourself in the foot but in particular to your customer experience should be what it should not be wedded to one model let alone even this current generation of models so with Sierra you can define your customer experience once in a way that's abstracted from all of the technology And it can be a chat, it can be, you can call you on the phone,
it can be all of those things, and as new models and new technology comes out, our platform just gets better, but you're not like re -implementing your customer experience.
And I think that's really important because, you know, we were talking about what's happened over the past two years, can you imagine if you're a consumer brand like ADT home security, and thinking about like, how can you maintain your AI in the face of all that, right, it's just not even, it's not tenable.
I mean it's not what you do as ADT.
So they worked with us to build their AI agent.
How do you fend off complacency?
Like a lot of these companies, and maybe not in tech specifically, but they get big, they get dominant, and then they take their foot off the gas, and that opens the door to competitors.
And there's just like a natural entropy almost to bureaucracy in some of these companies, and the bureaucracy serves the seeds of failure and competition.
How do you how do you fend that off constantly?
It is a really challenging thing to do at a company one of the, there's two things that I've observed that I think manifest as corporate complacency.
One is bureaucracy, and I think the root of bureaucracy is often when something goes wrong companies introduce a process to fix it and over those like sequence of 30 years the layered sum of all of those processes that were all created for good reason with good intentions end up being a bureaucratic sort of machine where the reasons for many of the rules and processes are rarely even remembered by the organization, but it creates this sort of natural inertia.
Sometimes that inertia can be good.
You know, it's like, you know, if you end up with you, there's definitely been stories of executives coming in and ready, fire, aim new strategies that backfire massively.
But often it can mean in the face of a technology shift or a new competitor, you just can't move fast enough to to address it.
The second thing that I think is more subtle is as a company grows in size, often it's internal narrative can be stronger than the truth from customers.
I remember one time when this sort of peak of the smartphone wars, and I end up visiting a friend on Microsoft's campus.
And I got off the plane and, you know, Seattle -Tacoma airport, drove into Redmond, went onto the campus.
And all of a sudden, everyone I saw was using Windows phones.
I assume it must have been a requirement or formal or social, like you were definitely uncool if you were using anything else.
And from my perspective at the time, like the war had already been lost. Like it was definitely a two horse race between Apple and Google on iOS and Android.
And I remember sitting in the lobby waiting for my friend to get me from this security check -in.
and I made a comment, like it wasn't a confrontation, but I made a comment to someone who was at Microsoft, I was like, something along the lines, are you required to use Windows Phones?
How are these other, and I just sort of was like curious, and then I got a really bold answer, which was like, yeah, we're gonna win, we're taking over the smartphone market.
And I didn't say anything, because it was a little socially awkward, I was like no you're not, you lost four years ago.
but there's something that's happening that's preventing you from getting reality.
Well and that's the thing is if you think about it, if anyone, if you've ever worked for like a large company, you know, when you work at a small company, you care about your customers and your competitors and you feel every bump in the road.
When you're a, you know, junior vice president of whatever and you're, you know, eight levels below your CEO And you have a set of objectives and results.
You might be focused as I want to go from junior vice president to senior vice president.
That's what success looks like for me.
And you end up with this sort of myopic focus on this internal world.
In the same way your kids will focus on the social dynamics of their high school, not the world outside of it.
And it's probably rational, by the way, because like, probably their social life is more determined by those 1 ,000 kids in their high school than it is all the things outside.
But that's the life of a person inside of these big places.
And so you end up where, you know, if you have a very senior head of product who's like this competitor says they're faster, but this next version we're so much better, and then everyone says, and all of a sudden that's like the Windows phone is gonna win.
That's what everyone says, and you truly believe it because everyone you meet says the same thing and you end up reflecting customer anecdotes through that lens and you end up with this reality distortion field manifested from the sum of this myopic storytelling that exists within companies.
What's interesting about that is the ability for a culture to believe in something is actually a great strength of a culture but it can lead to this as well.
And so the combination of bureaucracy and inaccurate storytelling, I think, is the reason why companies sort of die and, and it's really remarkable to look at, you know, the Blackberries of the world or the Tivos or the, you know, you can really, you know, as the plane is crashing like tell the story that you're not and, and, and, and then similarly, as I said, like culturally, you can still have like the person in the back of that crashing and plan being like when am I going to get promoted SVP and you're like you know yeah and and that's I mean this is like I've seen it a hundred times and so I
think it really comes down to leadership you know and I think that one of the things that most great companies have is they are obsessed with their customers and I think the free market doesn't lie and so I think one of the most important things I think for any like enduring culture particularly an industry that changes as rapidly as is how close are your employees to customers and how much can customers like the direct voice of your customers be a part of your decision making and that is something that I think you need to constantly work out because that person, employee number 30 ,462, how does
he or she actually directly hear from customers, it's not actually a simple question to answer.
is it direct, is it filtered, how many filters are there.
That's exactly right and then I think the other part on leadership is we talked about bureaucracy is process is there to serve the needs of the business and often mid -level managers don't get credit for removing process, they often are held accountable for things going wrong and I think it really takes top -down leadership to remove bureaucracy.
And it is not always comfortable when companies remove spans of control or all the people impacted, it's like antibodies, and for good reason, it makes sense.
Their lives are negatively impacted or whatever it is, but it almost has to come from the top because you need to give air cover almost certainly something will go wrong, by the way I mean like processes usually exist for a reason.
But when they accumulate without end, you end up with bureaucracy.
So those are the two things that I always, you could smell it when you go into a really bureaucratic company, the inaccurate storytelling, the process over outcomes, and it's just, it sort of sucks the energy out of you when you feel it.
That's a great answer.
We always end these interviews with the exact same question, which is what is success for you success for me we talked about how I spend my time with my family at work is you know having a happy healthy family and being able to work with my co -friend or clay for the rest of my life making sir into an enduring company that would be success for me thanks for listening and learning with us the front of Street blog is where you You can learn more about my new book, clear thinking, turning ordinary moments into extraordinary results.
It's a transformative guide that hands you the tools to master your fate, sharpen your decision -making, and set yourself up for unparalleled success.
Learn more at fs .blog slash clear.
Until next time!
