The Exchanges

Q To what extent are all business problems engineering problems?

A That's a deeper philosophical question that I think I have the capacity to answer. Um, what is engineering? What I like about approaching problems, uh, as an engineer is, uh, first principles thinking and understanding, uh, 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 a 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 though with engineers who go into other disciplines is, um, sometimes you can overanalyze Decisions in some domains. Let's just take modern communications, which is driven in social media and, and very fast paced. Um, having a systematic first principles discussion about every, you know, tweet you do is probably not a great comm strategy. Um, and so, uh, and then similarly, um, you know, there are some aspects of say enterprise software sales that, you You know, are rational, but they're human, you know, like forming personal relationships, you know, and, 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 …

AI assessment note: “I think a lot can benefit from engineering, but I wouldn't say everything's an engineering problem”

Q Such an interesting front row seat 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, the Brian Chesky, you know, the founder mode. Do you think, talk to me about that.

A I have such a nuanced point of view on this because it is decidedly not simple. Uh, 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. Um, I think that that's how great companies operate. Uh, and when you, you know, proverbially make decisions by committee or you're more focused on process Less than outcomes. Um, that produces all the experiences we hate as employees, as customers, you know, that's the proverbial DMV, right? You know, it's like process over outcomes. Um, and then similarly, uh, you look at the disruption in all industries right now because of AI, you know, the companies that will recognize where things are clearly going to change, like everyone can see it. It's like a, you know, slow motion car wreck. Everyone knows how it ends. You need that kind of decisive breakthrough boundaries, layers of management to actually make change as fast as required in business right now. The issue I have not with Brian's 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 never worked with him in any meaningful way, you know, but he was sort of, uh, if …

AI assessment note: “broadly speaking, I really like the spirit of founder mode”

Q It's such an interesting front row seat. 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, you know, the founder mode. Do you think, talk to me about that.

A I have such a nuanced point of view on this because it is decidedly not simple. Uh, 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. Um, I think that that's how great companies operate. Uh, and when you, you know, proverbially make decisions by committee or you're More focused on process than outcomes. Um, that produces all the experiences we hate as employees, as customers, you know, that's the proverbial DMV, right? You know, it's like process over outcomes. Um, and then similarly, uh, you look at the disruption in all industries right now because of AI, you know, the companies that will recognize where things are clearly going to change, like everyone can see it. It's like a, you know, slow motion car wreck. Everyone knows how it ends. You need that kind of decisive breakthrough boundaries, layers of management, um, to actually make change as fast as required in business right now. The issue I have, not with Brian's 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 times, I haven't ever worked with him in any meaningful way, you know, but he was sort of, uh, if yo…

AI assessment note: “broadly speaking, I really like the spirit of founder mode”

Q You brought up first principles a couple 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?

A 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. Uh, most companies hadn't heard the phrase large language models or generative AI yet. And in two years you have ChatGPT becoming One of the most popular consumer services in history, faster than, than any service in history. And you have across so many domains in the enterprise, uh, really rapid transformation. The law is being transferred, transformed. Marketing is being transformed. Customer service, which is where my company, Sierra works, is being transformed. Software engineering is being transformed. And the amount of change in such a short period of time is, uh, I think unprecedented. Uh, and, uh, 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, uh, if you're 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. Uh, so, uh, as an example, uh, it's really interesting to me that With modern large language models, one of the careers that is being most transformed is software engineering. Uh, and, you know, one of the things I think a l…

AI assessment note: “thinking from first principles about why we're at this point and where it will probably be”

Q Let's dive into AI a little bit. How would you define AGI to the layman?

A I think, uh, a reasonable definition of AGI, uh, might be that Any task that a person can do at a computer, um, that system can do on par or better. Um, I'm not sure that's a precise definition, but I'll tell you where that comes from and its flaws, but there's not a perfect definition of AGI, um, in my opinion, uh, or there's not a precise definition of AGI. I'm sure there's good, good answers. One of the things about the G and AGI Is about generalization. Um, so can you have a system that is intelligent in domains that it wasn't explicitly trained, um, 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, uh, become, uh, more competent and more intelligent than a person sort of trained in that domain? Um, and Uh, and I think that's sort of the, you know, 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, like, if there's a digital interface, um, to that system, um, it affords the ability for AI to interact with it, which is why that's a bar that's, uh, reasonable to hit. Um, I say that because one of the interesting questions around AGI is how quickly it does generalize, um, and, uh, there are domains in, uh, the world that, um, The progress in that domain is…

AI assessment note: “Any task that a person can do at a computer, that system can do”

Q would you think would help create that? How would you bring investment from all over the world into that country? And researchers, right? Like, 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?

A 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, um, which is a noun that wasn't a noun until recently, but now compute is a noun. And, uh, you know, I do think that's one area where policymakers can, um, uh, because it involves a lot of things that, uh, touch, uh, Federal and local governments like power, land, um, and then similarly attracting the capital, which is immense to finance, uh, to the real estate, to purchase the, uh, you know, uh, compute itself, um, and then to sort of operate the data center. And again, there's really immense power requirements, uh, for these data centers as well. Um, and then, you know, it's attracting sort of the right researchers and research labs to, you know, Leverage that. But in general, where there is compute, the research labs will find you, you know, and so I think that's it. And then there's a lot of national security implications too, just because, you know, these models are very sensitive, at least the frontier models are. And so, um, you know, how you, your place in the geopolitical landscape is quite important. Like will research labs and, uh, will the U S government be comfortable with training happening there and, and export restrictions and things like that. But I think a lot of it comes down to infrastructure, uh, as it relates to…

AI assessment note: “one of the key ingredients to modern AI is compute”

Q 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.

A You know, um, I wrote a lot of it too. So yeah, I'm sure there was some around it, but actually I think good teams want to do great work. And so, um, Uh, I think there was a lot of people constructively dissatisfied with the state of things, too, and, um, uh, you know, I think, uh, you know, the engineer had written that XSLT transform, I think, was like, you know, a little bit, it'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, you know, 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, uh, impede forward progress. Um, and, you know, 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'll probably be a more monolithic model that, uh, encodes many of the same rules. You have to thr…

AI assessment note: “yeah, there might have been some feathers ruffled, but at the end of the day”

Q a lot of these companies, and, 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 like a natural entropy almost to bureaucracy in some of these companies that, and the bureaucracy sows the seeds of failure and competition. How do you, how do you fend that off constantly?

A It is a really challenging thing to do at a company. Um, one of the, there's two things that I've observed that I think manifest as corporate complacency. One is bureaucracy. Um, and I think the root of bureaucracy is often, uh, when something goes wrong, companies introduce a process to fix it. And over those, like, uh, 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, um, bureaucratic sort of machine where, um, the reasons for many of the rules and processes are, are rarely even remembered by the organization, but it creates this sort of, uh, natural inertia. Um, sometimes that inertia can be good. You know, it's like, you know, if you end up with, uh, you, you've, there's definitely been stories of executives coming in and ready, fire, aim, new strategies that backfire massively. Um, 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, uh, often its internal narrative can be stronger than, uh, The truth from customers. Um, I remember one time, uh, when this sort of peak of the smartphone wars, and I ended up visiting a friend on Microsoft's campus, and, uh, I got off the plane and, you know, Seattle-Tacoma Airport, drove into Redmond, went…

AI assessment note: “there's two things that I've observed that I think manifest as corporate complacency.”

Q I love that response. How do you balance, uh, having a young family with also running a startup again?

A I work a lot. Um, I don't, uh, I really care and love, care about and love working. Um, so, um, One thing is that I, um, well, there's always trade-offs in life. Um, if I didn't love working, I, I wouldn't do it as much as I do, but I, I just love, uh, love to create things and love to have an impact, and so I, like, jump out of bed in the morning and, um, 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, I basically work and spend time with my family. Um, the first time we talked, you saw a couple of guitars in my background.

AI assessment note: “I don't have a ton of hobbies. You know, I basically work and spend time with my family.”

Q 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 the world where, um, 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?

A I'll answer broadly and then go into the regulatory question. So, I really like OpenAI's mission, uh, which is to ensure that AGI benefits all of humanity. That isn't only about safety, um, and, and I, and I believe intentionally so, though, the, obviously the mission was created prior to my arrival, because it's both about safety, kind of Hippocratic, oh, first do no harm, and I don't think one could credibly achieve 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 benefiting humanity. Um, is it universally accessible? Is there a digital divide where some people have access to AGI and some don't? Um, uh, similarly, you could argue that does it, are we maximizing the benefits and minimizing the downsides? Clearly, uh, AI will disrupt some job, but it also could democratize access to healthcare, education, expertise. Um, 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, benefiting humanity is the mission, and, um, uh, safety is a prerequisite, but it's almost like going to my analogy of the Hippocratic Oath. A doctor's job is, you know, uh, to cure you. First do no harm, but then to cure you, and a doctor that did no harm but didn't cure you wouldn't be great either, so I really like…

AI assessment note: “I'll answer broadly and then go into the regulatory question.”

Q How did you, 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 while you were there too. How did that change how you went about integrating that company into the Salesforce culture?

A I'll talk abstractly about talking about some specific acquisitions too, but first I, I think I tried to approach it with more empathy, um, and more realism. You know, uh, one of the nuanced parts about acquisitions is there's the period of, um, doing the acquisition. There's sort of the period, uh, 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 the company and sort of the period after. One of the things that I have observed is that, uh, companies doing acquisitions, often the part of deciding to do it is a bit of a mutual sales process. Um, uh, 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 usually some objective measure of that, um, influenced by a lot of factors, but, but there's some fair value of that. But what you're trying to do is what are the Uh, and corporate speak could be synergies, but like, why do this? Why is one plus one greater than two? You know, that's, that's why you do an acquisition just from first principles. It's often a exercise in storytelling. You know, uh, you, you know, bring this product together with our product and customers will, you know, find the whole greater than the sum of its parts. This team applied to our sales channel or via Google acquisition. You know, imagine the traffic we can dri…

AI assessment note: “I think I tried to approach it with more empathy, um, and more realism.”

Q It's so interesting that if software AI is good enough to write the code, should we get enough to check the code?

A That's a great, um, great question, but actually I'll, you know, it's still funny to me that we'd be generating Python, you know, just because for anyone who's listening right now has ever operated a web service running Python, it's CPU and intensive, really inefficient, 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? Uh, 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 in software development came from breakthroughs in computing. So, You know, the C programming language came from Unix, and, ah, when these timesharing systems were really, it went from sort of punch cards to something that were, were a lot more agile. Um, small talk came out of the development of the graphical user interface at Xerox PARC, and, you know, there was a, ah, sort of a confluence of message passing as a metaphor and, and the graphical user …

AI assessment note: “I do think it can be turtles all the way down. You can use AI”

Q 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, you know, a hundred billion dollars 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?

A 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 that I think are useful. Um, 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, 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. Um, and so it's truly foundational in many ways. In the same way, most people don't build their own servers anymore. 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. Um, And I think that leads 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, ah, you know, building the huge clusters to, 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, to Reid Hoffman, but, uh, I may be …

AI assessment note: “let me just start with a couple definitions of terms that I think are useful”

Q 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?

A Well, first, I think OpenAI, I believe, will play a huge part in it, because, uh, there's both the technology, which I think OpenAI continues to lead on, um, but also ChatGPT, which has become synonymous with AI for most consumers, but more than that, um, it is the way most people access AI, um, today, and so, one of the interesting things, like, what is AGI? We talked about, you know, opinions on, you What the definition might be, but the other question is like, how do you use that? Like, what do you, what is, uh, what is the packaging? Um, and some of, uh, intelligence will be simply the outcomes of it, like, uh, discovery of a new drug, which would be, you know, remarkable, and hopefully we can cure some illnesses. Uh, but others will be just how you as an individual access it, and you know, I, most of the people I know, like, if they're signing an apartment lease, we'll put it in the chat, GPT, you get a legal opinion, Uh, if you get, you know, lab results from your doctor, you can get a second opinion on, on ChatGPT. Um, Clay and I use, uh, the O-one pro mode for, like, criticizing our strategy at CIRA all the time. And so, for me, what's so remarkable about ChatGPT, which was this, you know, quirkily named research preview that has come to be synonymous with AI, is I do think that, It will be the delivery mechanism for AGI when it's produced, and, uh, not just because of …

AI assessment note: “Well, first, I think OpenAI, I believe, will play a huge part in it”