No Priors: Artificial Intelligence | Technology | Startups - Transforming Customer Service through Company Agents, with Sierra’s Bret Taylor

Episode Date: September 19, 2024

Bret Taylor, Cofounder of Sierra, Chairman of the board at OpenAI, and former co-CEO of Salesforce and CTO of Facebook, joins Sarah and Elad in this week’s episode of No Priors. Bret discusses building company-branded AI agents with unique personalities, goals, and guardrails at Sierra, and their potential to revolutionize customer engagement while cutting costs. The conversation explores the next sectors for enterprise AI adoption, building resilient AI products, and the parallels between today’s AI market and the evolution of the cloud industry. Bret also shares his unique insights on future business models and upcoming technology shifts. Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Btaylor Show Notes: (0:00) Intro (0:42) Defining agentic systems and types of agents (3:55) Customer-facing company agents (5:43) Sierra AI (8:11) Transforming customer service and reducing costs (9:57) Challenges in implementing LLMs for company agents (14:45) Drawing parallels between AI and the cloud market’s evolution (17:50) Future of the AI landscape (19:15) Building durable AI products (24:39) Outcome-based business models and tangible ROI in AI solutions (29:22) Next wave of AI sectors for enterprise adoption (31:15) Customizing goals and guardrails with customers (35:55) Creating distinct personalities for Sierra's agents (41:05) Bret’s insights on upcoming technology and hardware shifts (46:50) How AI software could enhance human agency

Hi, listeners. Welcome back to No Pryors. Today we have Brett Taylor, whose legendary career spans from creating Google Maps to serving as the CTO of Facebook and COSI of Salesforce, founding two companies along the way, as well as chairing the board of Twitter and now Open AI. He and Clay Bevoir have started Sierra, which is creating company agents for the next generation of customer experience. experience. I'm thrilled to have such an amazing technologist and leader at all skills with us today and longtime friend. Welcome Brett. Thanks so much for joining us today, Brett. My pleasure. Thanks for having me. Let's get right into it. Do agents work today? How do you define agents? Or do you

define agents? You define agent. You're the expert. Agents mean something different in academia than I think they mean in industry right now. I think both definitions are important. Just starting what I view as sort of the classic academic definition is an agentic system is one where software can reason and take action autonomously. And it comes from the word agency. And as a consequence of such a broad academic definition, I think it becomes sort of the proverbial inkblot test for people using the word. In industry right now, there's probably three categories of agent that I think are or on the cusp of working. The first, I think, which a lot of people online talk a lot about as personal agents, and I think that's probably the earliest of the three categories that I see,

but maybe one of the more exciting ones. And this is the agent that will triage your inbox, schedule a vacation, help you prep for a meeting, manage your calendar, all of that. And the reason why I think that's earliest, I think it's really interesting to make some demos, but I think the human-computer interaction and even how the agents interact with all the systems we depend on as people is quite complex. you can think of sort of the surface area of both reasoning and systems integrations is almost infinite. And so as a consequence, I think it probably a prerequisite for a great personal agent

probably demands more technology than is currently available, though there's lots of interesting startups in this space. And you could imagine some interesting companies carving out meaningful niche use cases that expand as the technology improves. The second category of agent, I think this one does exist in some categories, is what I call persona-based. agents. So they're agents that do a job, a very specific job. You know, there's companies like Harvey that, you know, serve a legal function. There's all the coding agents. I think there's some fairly effective ones right now that serve the job of a computer programmer. I think this is really exciting because I think when you narrow, I call those cases narrow but deep. If you're just

trying to. Both task scope and perhaps integration scope. That's right. The tools you access and even how you evaluate the effectiveness is, you know, if you're building a coding agent, there's actually really good benchmarks already. Similarly, compilers emit error messages and you might have integration tests. You end up with this scaffolding that actually, practically speaking, limits the scope of sort of the true research that you have to do to accomplish it. I think broadly speaking with the advent of foundation models, a lot of effective AI right now is where you've taken areas of research and you made them areas of engineering. And I think you can engineer very effective. perspective persona-based agents for certain domains where the technology applies, like the law,

like areas of software engineering and things like that. My take is the domain of personal agents is probably of the very large consumer companies like Apple and Google and OpenAI and others that have big consumer brands for the persona-based agents. I think there's probably meaningful companies in each of those spaces because I think to do those effectively it involves sort of the confluence of AI expertise and expertise in that domain. The other category, which is the area that my company, Sierra works in, is what I call company agents. And it's really less simply about automation or autonomy, but in this world of conversational

AI, how does your company exist digitally? I always use the metaphor. If it were 1995, you know, if you existed digitally meant having a website and being in Yahoo directory, right? In 2025, existing digitally will probably mean having a branded AI agent that your customers can interact with to do everything that they can do on your website, whether it's asking about your products and services, doing commerce, doing customer service. That domain, I think, is shovel-ready right now with current technology because, again, like the persona-based agents, it's not boiling the proverbial ocean technically. You know, you have well-defined processes for your customer experience, well-defined systems. are your systems of record.

And it's really about saying in this world where we've gone from websites to apps to now conversational experiences, what is the conversational experience you want around your brand? And it doesn't mean it's perfect or it's easy. Otherwise, we wouldn't have started a company around it, but it's least well-defined. And I think that right now in AI, if you're working on artificial general intelligence, your version of agent probably means something different, and that's okay. That's just a different problem to be solved. But I think, you know, particularly in the areas that Sierra works and a lot of the companies that you all have invested in, is this saying, you know, are there some shovel-ready opportunities right now with existing technology?

And I absolutely think there are. Can you describe the, like, a shoveling cycle of building a company agent? Like, what is the gap between research and reality? Like, how do you, what do you invest in as an engineering team? Like, how do you understand the scope of different customer environments? Just like, what are the sort of vectors of investment here? And maybe you're sorry to know, but as a starting point, it may even be worth also defining, like, what are the products that Sierra provides today for its customers? And then where do you want that to go? And then maybe we can feed that back into, like, what are the components of that. Because I think, obviously, folks are really emerging as a leader in your vertical, but it'd be great just for a broader audience to understand what you focus on. Yeah, sure. I'll just give a couple examples to make it concrete. So if you buy a new Sonos speaker or you're having technical issues with your speaker, you get the dreaded flashing orange light. You'll now chat with the Sonos AI, which is powered by Sierra to help you online. board, help you debug whether it's a hardware issue, a Wi-Fi issue, things like that.

If you're a serious XM subscriber, their AI agent is named Harmony, which I think is a delightful name. And it's everything from upgrading and downgrading your subscription level to if you get a trial when you purchase a new vehicle speaking to you about that. Broadly speaking, I would say we help companies build branded customer-facing agents. And branded is an important part of it. It's part of your brand. It's part of your brand experience.

And I think that's really interesting and compelling because I think just like, you know, when I go back to the proverbial 1995, you know, your website was on your business card. It was the first time you had sort of this digital presence. And I think the same novelty and probably we'll look back at the agents today with the same sense of, oh, that was quaint. You know, I remember if you go back to the Wayback Machine, you look at early websites, it was either someone's phone number and that's it or it looked like a DVD intro screen with like lots of graphics, you know,

lot of the agents that customers start with are often around areas of customer service, which is a really great use case. But I do truly believe if you fast forward three or four years, your agent will encompass all that your company does. I use this example before, but I like it. But just imagine an insurance company all that you can do when you engage with them. Maybe you're filing a claim. Maybe you're comparing plans. We were talking about our kids earlier. Maybe you're adding your child to your insurance premium when they get old enough to have a driver's license. All of the about you know, all of the above will be done by your agent. So that's what we're helping companies build. And Sierra's initially focused on facing, like, consumer-facing companies.

Yeah, the vast, vast majority of our customers are consumer companies. Technically speaking, there's not a huge difference between sort of a B2B company and a consumer company, except for the volume of customers that you have. And I always like to think from first principles about what does this technology enable that was impossible before? And if you think about the typical cost of a conversation. So if you call into a call center today, one of the key metrics for most service teams is their cost per contact, which is like, what is the all-in cost of the labor and the technology to fulfill that phone call? For most phone calls, it's called $13, you know, to service that phone call. Now with AI, you can bring down that cost to well below a dollar, you know,

and so all of a sudden you've literally decreased the cost of a conversation by an order of magnitude. And so if you're just doing the math on that, like what companies would benefit most from that cost? And, you know, I'm not sure depending on the math equations, see that the numerator of the denominator, but if you're measuring in millions of consumers, obviously the value is really different for consumer companies. For a lot of consumer brands, because having conversations is a really expensive thing to do, you don't necessarily make it easy. You know, there's entire websites devoted to finding companies phone numbers because often in some ways a really consumer friendly you push these towards these digital self-service experiences I'm really excited about now that

having conversations with your customers is an order of magnitude cheaper maybe you can do an order of magnitude more you know what does that actually mean so with these technology trends I think you often start with just digitizing what you currently do but I actually think the second order effect will be gosh now that having a conversation isn't a formidable cost center, how do I actually want to incorporate having a conversation as a key part of my customer experience? So going back to your question, I think that's a much more meaningfully different conversation with a large-scale consumer company than it is with, say, a B2B company with 100 customers. It doesn't mean it's not valuable. I just say the level of impact

and the difference in the decisions you make are quite different. Can you describe some of the key challenges in like taking the capabilities of foundation all today and then making them work in of like the company agent context? One of the techniques, and I think you all have probably talked about on your podcast that's very common today is it's what's called retrieval augmented generation. And essentially what that means is you take a large language model and rather than using the model and it's innate knowledge from the pre-training process to emit answers, you combine that model with a database of content and you say use the content as a source of truth and you

asked the model to summarize selected content from that database. That's kind of a roundabout way saying if you can ground the agent and knowledge that you provide it, but also you can take off-the-shelf models and integrate it with proprietary business data. So it's a really popular technique right now. I would say that's a really exciting area, but what we found in practice is that broad category of technology investment is woefully insufficient for almost any meaningful customer experience. If you think about, you know, all of the interactions you've had with brands that you care about, what percentage of those conversations were asking questions? Probably none of them. It's all about taking action, right? It's upgrading or downgrading a subscription. It's returning in

order. It's a warranty exchange. It's a, you know, filing a claim with an insurance company. All of those are not only not simply answering questions, but also taking action against probably 10 plus systems of record. It's probably a very complex process. Often that process has both business goals. You know, how do we, you know, prevent you from canceling or convince you not to? There's probably compliance goals. If you imagine being a, you know, hippo-compliant, you know, a health care adjacent firm. You know, there's a lot of restrictions on what you can and can't do. You might be in a truly regulated industry. And all of that means that, you know, this idea of sort of building agents that can be grounded in content is a great demo, but actually not

necessarily an impactful product. That's the air of technology we've really tried to solve. We are really trying to create a platform where you can orchestrate a process of arbitrary complexity, not simply have agency in the AI, but also have guardrails as well. Broadly speaking, most software systems for the past two decades have been rules, engines that execute really quickly, whether the rules are implemented as source code or perhaps in a low-code platform. And now we're moving to a world of goals and guardrails. And so people, businesses now have the opportunity to express a business process,

not simply as a set of rules and a decision tree, but saying, what are you trying to achieve? Where do you want the AI to have agency, i.e., where do you want it to have creativity? And where do you not want it to have creativity? And it's a remarkably interesting technical problem. It's also a remarkably interesting, I would say, social and business problem. A lot of companies will start out saying,

I want to control precisely what the AI does, which is a fine goal. And actually, our platform does support it. But if you do that, it can be fairly robotic. And you're actually removing a lot of the magic that people feel when they engage with things like chat, GPT, which is fundamentally the creativity and agency innate and some of these models.

On the other hand, if you turn that knob up to, you know, this is spinal tap 11 on agency, you know, you could get hallucinations. It could violate your policies, or more subtly, it could just not be a great brand ambassador, you know, for what your brand does. So I would just say that I think there's a really deeper thing that we're trying to build, which is how do you program against non-deterministic creative software? What are the abstractions that we need to build to express goals and guardrails so that you don't remove the creativity and the agency that I think make these experiences delightful? It's why ChatGBTGBT got to 100 million users faster than any service in history. But also, you can represent to your board, your CEO, your customers,

that there's the right guardrails in place. And then there's like where you actually are comfortable. Where are you comfortable with this AI having agency? So it's a really fun technical problem. I think it's also a really new design problem, almost a philosophical question about where you want to seed certain amounts of creativity to software in a way that just wasn't a conversation one could, have more than a couple years ago.

How much do you think those different aspects you mentioned, the guardrails, or in some cases I've seen people working on agents to build their own reasoning engines and other things, their own modules that go on top of the core foundation models or LLMs? How much of that do you think as a company you need to keep doing yourself versus we'll eventually get integrated into the core model companies like Open AI or Anthropic or people like that? If you don't mind, I'll zoom way out for a second to give you my view of the marketplace. And then I'll jump into that question.

There's a Mark Twain quote, history doesn't repeat itself, but it rhymes. I think the AI market will rhyme with the cloud market of the past 15 years. And if you look at how that played out, broadly speaking, you ended up with a small handful of infrastructure as a service providers that represent the vast majority of the KAPX investment in cloud, i.e., most software as a service companies pay rent to one of those infrastructure providers like Amazon Web Services or Azure or Google, cloud. And again, because there's economies of scale and data center development, it didn't make sense for a startup to either build their own data center or for a startup to actually build infrastructure as a service business, just the capital expenditures required. And that positive feedback loop on CAPEX just didn't work out. I think that will probably play out

with the frontier models. We'll end up with a relatively small number of companies doing pre-training, you know, which is the really capital-intensive part of model building, not because, you know, they're the only places with good researchers, but again, if you look at the CAP-X requirements to actually make a return on that CAP-X, it really, you want to lease it out to a large number of people, and then for a lot of companies that, especially startups who have done pre-training, they're finding, like, the making a return on that is questionable mathematically. Do you think of the long run that just ends up being the main? cloud providers or cloud providers plus one or two other players because fundamentally to your

point there's a capax and ability to afford it side the second piece of it is if you're actually running all your application all the data everything else in one of these cloud providers pinging out to a third-party service just adds latency so you add the round trip you add a second sort of buying behavior around approval budget security etc so do you think it's just going to roughly consolidate around the clouds plus or minus I do think it will roughly end up the cloud providers in partnership with the big research labs, which is roughly the current, you know, landscape. I'm not sure I completely agree on the security and latency front. It's possibly true. It was interesting. I think that, you know, most companies, most large enterprises now use

multiple cloud providers. Most of them use software as a service and don't necessarily care where it's hosted as long as the security and reliability requirements are met. And there's obviously some exceptions to this, but I think thanks to 20 years of software as a service, people have sort of evolved their expectations to not ask, you know, where do you get your power and just say, what is your, you know, SLA for this service? And I think that's probably a positive trend. So I do think there's probably meaningful latency and security issues to overcome, but that all being in the same substrate, I'm not sure I make that leap. I might be wrong. I just, you know, I view the evolution of software as a service having evolved that. But going back to

my history rhyming point, I think you'll have a relatively small number of foundation model builders and doing pre-training. I think there will be a market of tools companies. You know, what great one in AI might be scale AI. You know, Snowflake was a great example in cloud that might also be an example in AI. And I think all those tools companies, and it's the proverbial pickaxes in the gold rush. If you're trying to transition to the cloud, what software do you need? You're trying to transition to using AI in your business, what are the tools and software that you need? And then the final category would be solutions. Just like in the cloud era, you can take the services from Amazon Web Services or Azure or GCP and build almost anything, but most

companies don't want to. Most companies want to solve a problem. And the total cost of ownership of building your own CRM or ERP system is nonsensical. And I think it took a long time for companies to realize that, but certainly they have now. I think the same will largely be true of AI. You know, if you want to, you know, automate customer service working with Syria is much easier and lower costs than billing yourself. If you want to, you know, automate parts of your legal process, talking to Harvey is probably a much more logical path than trying to roll your own for all the same reasons it was true if software's a service. So broadly speaking, going back to your question, you know, how do you build technology and what will the foundation model,

do, I think the higher order bit is what is the value you're providing and how do you decouple and are you adding enough value on top of models to be a real company? And the answer is if every time there's a new release of an AI model somehow decreases your value, it probably indicates you're not actually a solution. You're, it might be a slight value add on top of the models. I think there's a number of startups that unfortunately sort of smell like that. You know, it's not necessarily a lot of value. But what happens for Sierra when models improve? If we're doing our job right, our platform gets better. So, you know, I think that our customers, you know, in our platform, which we call Agent OAS, are essentially defining the goals and their guardrails of their customer experience.

And every time we have new technology available to make that work more effectively, we plug it in and, you know, you get better case resolution, better customer satisfaction, you know, fewer negative experiences. And that's just great. In the same way, you know, when any web service that you provide from a software as a service company just gets. better when the technology gets better. That's effectively we want to provide. But what our customers are hiring us to do is not related to the models. It's related to their customer experience. So fundamentally, that's the way we think about it. And as an entrepreneur, I would, you know, I think there's a danger if you don't fit into one of those lanes. At least that's my opinion because there's a real question of, you know, when a model improvement comes out, you know,

if that was 50% of the value provided, you, you're in the sort of uncanny valley of value. But I do you think the idea that all use cases will come from foundation models is probably wrong. I mean, it's hard to predict the future right now. But I think that would be the equivalent of saying, you know, 15 years ago, gosh, there's not going to be a single software as a service company. Everyone's just going to build their own or from the Lego bricks provided by. There were enterprises that said that. Yeah.

Yeah. Yeah. For a long time. I actually think it perhaps the opposite came true there. And, you know, most businesses, like, I would like to know, like, where do you want to innovate? You know, like, with the relatively few engineers you have, if you're a large retailer, you don't have the resources to implement everything yourself. Like, where do you want to stand out?

Where do you want to stand apart? And for most companies, you know, they benefit from the rising tide, lifting all boats, have invested in a software as a service platform. I just see the same thing happening here. So I'm very bullish on the, like, going back to our definition. of agents, you know, all the companies creating persona-based agents, they'll obviously compete with each other. But I think there's meaningful, you know, companies in that space, and I would probably work with them over assuming it's coming from the foundation model

providers because they're solving all the unique problems. This would take a coding agent of developer workflows, of security, of different programming languages, all these things. I actually think there's a ton of value there. And I also think there's probably second-order effects of relying on coding agents, how they incorporate into your team, code reviews, all these things that I'm not necessarily thoughtful enough to enumerate right now, but that's why there's a company in this space. And I'm very bullish on that company existing for the long term, not even knowing half the names. I think to your point, the analog with SaaS is a really telling one because people always talk about wrappers on foundation models and how those

companies will go away. And you could argue that a lot of SaaS is like a wrapper on a SQL database. It's kind of like the same thing. Yeah, I think the same was true, probably said of Shopify, Salesforce service now. And those are all great companies, you know. It's interesting. They ended up being very, like, let's say, the database vendor to Salesforce and of being a very important vendor for a long time. And, like, it did actually get yanked out eventually.

For what it's worth, like, what's really interesting about the cloud market, if you buy my analogy, and analogies are always sometimes dangerous, too. And let's say the exceptions to the analogy come sometimes a bit hidden. But, you know, the foundation model providers will all benefit from this investment. You know, these, I really do think these foundation models have a ton of. of innate value. And, you know, so any solution built on top, the foundation model providers will collect attacks on all those amazing use cases, and that's really great for everyone involved. As you sort of alluded to, you know, different application companies can decide to

use different models at different times. So it creates a lot of probably healthy competition there as well. And the most important thing for, say, Sierra customers is we're future-proofing our customers from, you know, that. Both future-proofing so you don't end up in the, you know, dreaded situation of something breaking when new technology comes out. But in a more meaningful way, when there is great new technology coming out, can I just turn it on? You know, can I benefit from it? And I think for a lot of the solutions and applications companies in this space, that will end up over time one of the main values they provide. You know, for anyone who's experimented with prompt engineering or prompt engineering with tool use and kind of, I would say, the low level of these

models, it's not like your prompt to just work with future. I mean, the model could, be better, but it's not strictly better. It's actually, you know, there's a very tight fit between the tokens and the model and all these things. And, you know, well, there's lots of interesting tools there. I'm not sure that's like the layer that most companies should or will want to be operating. Just like, you know, your company doesn't want to know that you're doing a data brace migration. It's boring but important. And, you know, what software as a service provides is you don't need to care about that. Just no downtime. Yeah. The other thing that's interesting is business model. At Sierra, we're really focused on what we call outcome-based pricing,

you know, charging for the job done. I see a lot of startups in this space doing it. That's another really powerful part of software as a service in the era of AI is I think you can, you know, I think the best AI companies are aligning their business model with their customers' business models, charging for the outcome, you know, and I think that's a really powerful new business model, maybe as powerful as the idea of subscription-based software and the era of software as a service, again, providing out-of-the-box solution and aligning, you know, the actual business model of your company with the outcome is very meaningful and it's very meaningfully different than paying for tokens, you know, and I think actually building that alignment with your customers is valuable

as well. Also, commitment to that suggests, like, a lot of confidence and ambition and, like, how valuable these solutions can be as well. It's not trivial. But one of the most exciting things about solution companies, application companies in the landscape is like you do see like magnitudes of like value improvement versus the existing solutions. Yeah, I think for if you talk to economists, they'll talk about software as drivers of productivity and sometimes in a very abstract way. And certainly if you, you know, sometimes it's really obvious. Like I can't remember it, but the pre and post Microsoft Excel in finance department. It has to have driven just first reasoning, like a slide rule versus Excel or a calculator versus Excel, like, of course it drove productivity.

But for the past 20 years, it's been quite indirect, you know, and everyone who's listening... Or incremental gains. Or incremental, but every person who's listening who's been like an enterprise sales cycle has presented some slide on return on investment, ROI. And there's all these like ROI calculations. And you spend all this time trying to like, you know, if you get every person gets 5% more of this. And I don't want to say it's BS, but it's like, you know, I think a lot of procurement and IT folks have seen like a hundred of those presentations. And you're like, did we actually decrease the number of people in the department?

Did we actually measure those things? I actually think in the age of AI, because these systems can autonomously take action with the appropriate guardrails, we're closer to actually software actually doing a job that's quite measurable. If there's an analogy, I sort of reminds me a little bit of going from impression-based ads to cost per click-based ads. It doesn't mean you're totally towards the transaction, but you're getting closer. And in that transition,

which Elon and I sort of lived through at Google, customers are just willing to pay disproportionately more for the click because even if you could sort of halfway measure some of the impression stuff, the closer you are, the direct attributions is worth a ton. And it's a great thing for companies right now that you should be holding your software, you know, providers to a higher standard, you know?

and you should get closer to the value. And I actually think that's a great trend. And going back to our analogies of legal and coding and service, you can actually see the value. It produces this function. You know, it actually analyzed this contract. You know, it did this thing. And you're like, I actually know how to value that.

Like, we've been valuing that in our employees for a long time. You know how much you'd have to pay a consultant to do X, Y, or Z. You know your cost per contact in your call center. And that's really remarkable. I think that's going to really change the relationship between software vendors and companies. I think it will really make software vendors true partners to the companies they work with have done appropriately because you're actually delivering valuable. It's actually measurable.

I think it's an incredibly positive change because you talk to any CIO and you ask them, are you getting the value you hope from all the software you purchase? You'll see like the blood drain in their face. They'll have horror stories, right? Of, you know, the difference between the... Dejection. Yeah, the dejection. And it's complicated. I think this is a really positive trend.

Maybe a very high profile example of that was Klarna, where they publicly talked about how implementing effectively customer support workflows for their own business, I think, ended up with dramatically higher net promoters scores, higher customer satisfaction, less time per customer. They basically automated a bunch of workflows. At the same time, they also reduced the size of the team by, I think, 700 representatives or people. And so it had a huge impact in terms of how their business functioned and how they were able to deal with customers and the languages they could support. poor people in and all the rest of it. So it seems like there are these very sort of prominent

examples now emerging in terms of this massive impact that you're talking about. I think the impact is here. And that's why I'm really excited about many of the companies sort of in the application space, because I think they're closer to the tangible value right now, as opposed to like broadly. What do you think are some of those other application areas? You mentioned what I view as sort of the three most popular ones right now in terms of adoption by enterprise, which is basically coding customer success. I think there's a lot of lot of effort right now ongoing and sort of sales productivity or sales and marketing productivity. Are there other areas that you think are, you mentioned legal? Are there other areas that you view

is sort of the most near-term next wave of these areas where, you know, it's very clear that these things will be very impactful? I'm not sure this is one job, but I'm really excited for automating the role of an analyst, especially back office analysis and not necessarily replacing, but sort of the Iron Man suit, you know, for analysts. If you think about the very superficially high-level role of an analyst. It's to synthesize complex data to provide insights to stakeholders. And, you know, if you think just first principles about what large language models are good at, which is summaries, synthesis, reasoning, I think there's really some interesting applications there. It does seem complex. You know, language models aren't necessarily

good at numerical or tabular data without a lot of work. Domain-specific data might have, you know, know, connotations or complexities that aren't necessarily present in foundation models on their own. So it strikes me as one of those areas like coding, like the legal, where actually there's benefits to fine tuning, there's benefits to domain specific expertise. As I said, I'm not sure analyst is a role. I think there's probably different departments have different analysts. But if you look at a company and a larger firm, you know, how many people's job it is to take data, make a presentation, you know, all these things. Do some transforms. Do some transforms. And again, I think that whether it's, you know, replacing, I'm not sure, but certainly augmenting and making that tremendously

more effective, more real time. I think that's really exciting as well. Can we go back to, you say goals and guardrails for a minute? Like, as you described, we're going away from, you know, complex rules engines as business software. That's like a pretty big mindset shift for your customers to ingest. How do you work with them on, I guess, evaluation of like how well Sierra agents work and get people comfortable with that? Yeah, so a couple of things. I'll describe technically and then talk a little bit more operationally as well. So technically, we work a lot with our customers to actually formalize and define their processes, you know, and sometimes our customers come in with really well-defined processes. Sometimes they don't. We like to say an agent's

made up of not only the factual knowledge, but the procedure. knowledge, you know, with the process as follows, and it gets into the integrations with systems. And we spend a lot of time talking about where do you want guardrails, where do you want creativity, and where do you want agency. And then we do a lot of experimentation, you know, in a proof of concept, have it live and actually through sort of this technology encountering the cold, hard reality of actual people, you know, did this actually meet the expectations you thought you had? And with that, we've developed a lot of tools for customer. experience teams. So we think that AI should not be the domain of technology teams exclusively.

You know, the team that owns your customer experience at your company, maybe it's in the office of the chief digital officer, maybe it's a formal customer experience team. They should be the ones with their hands on the steering wheel of these experiences. So we built a lot of tools and platforms where those teams can audit and improve the agent and actually have their hand on the steering wheel for, you know, what their agent does over time. And it's not a lot of something that's ever done. Going back to sort of the deeper question on making people comfortable, these are very organic systems. So if you just imagine you're a retailer and you go to a retail website, there's probably a menu somewhere on it that goes over all the categories you have, men, women,

shoes, pants, whatever it might be, and you click on them and it filters the listings. And there's sort of a standard retail template at this point. I'm not sure it's the best, but this is like the world that we live in. If you imagine having a conversational AI agent, it's a freeform text box. it is completely free form. So going back to my bad analogies, it's a little bit like going from Yahoo Directory to Google search. You know, you have a taxonomy of everything you can do, do you have a free form text box saying,

what do you want us to do? And as a consequence, it tends to be a lot broader than I think people originally contemplate. I think it tends to, there tend to be sort of a long tail of customer experiences that not only did we not design the agent for, but our customers did not anticipate either. And I think that's a really interesting, deeper question.

We talked about like a crazy, like, fraud return case where nobody knew what was going on. Yeah, exactly. I mean, there's just, it's a voice of your customer quite literally. So I think that's a really exciting dynamic. There's a book called The Long Tail, and I associated a lot with Google. I think maybe Eric Schmidt wrote the foreword to it,

if I remember correctly. But I do think as the world of the internet transition from directories to search, and the number of web pages increased, you ended up with not only big popular sites, but this long tail of blogs. And, you know, it was really, is and was a really remarkable part of the web. I think we're kind of moving towards that in customer experience,

where you curate the few screens available to your customers, and if you move to a world of an AI agent, you can just say, sorry, I can't help you with that, but probably what you will do is treat it more like paint by number. You know, wow, here's the thing. our customers want to talk to us about how do we fulfill that desire and that need. So it's a really interesting combination of customer insight, but also I think a very new way of developing customer experiences that is much more organic.

So it's going to be adaptive as people learn. Quite adaptive. It's an always-on system. And it's not just like running an AB test. It's a little bit less controlled. Like it's a system and organism that you're constantly. So a lot of our platform is how do you empower customer experience teams to manage that? The new edge case, the emergent customer behavior, not model behavior. That's exactly right. Like what new thing is happening in the wild today? External events, controversies, products that were popular that got changed.

And how do you not only just get insights from that, but how do you actually constantly evolve this agent in a way that doesn't remove the agency of the customer experience team whose job it is to define this, but also embraces the natural organic emergent behaviors. of AI. If you scroll forward, I don't know what it is, six months, 12 months, like, say we have the text box, right, voice mode is coming, video avatars exist now, is what we should expect that the Sierra avatar is like you or Clay or something like more personified and Richard, like does fidelity matter like that? It does. And actually, it's really fun to go to some of the Sierra agents in the wild and just see the radically different personalities in each of those agents, I think that your agent should be a brand ambassador. What's so remarkable about large language models is their ability to observe the sentiment of the person talking to it, you know,

because of instruction tuning, which is the mechanism of making these large language models conversational, they'll naturally sort of reflect back the sentiment and tone that you have. But you can also control it and modify it. So for your brand, if you want an irreverent brand, you can have that. If you want a more austere brand, you're like a little. luxury blend. You can have that. Do you do that as part of the prompts or do you do post training or how do you actually implement that into your product? A combination of all of the above. You know, there's some parts of tone and brand that are adequate for prompts depending on the models. There are some parts of brand that are more sensitive, you know, like you don't want

your AI agent giving medical advice or giving financial advice. And that's sort of tone, that sort of substance. And we do a lot of what we call supervisor models. So we have models supervising other models. It's turtles all the way down. Our joke in our office is the solution to every problem and AI is more AI, which is really exciting. And I think it is the fun part of our platform is, you know, we have a lot of tools at our disposal to solve these meaningful problems. I think it is really exciting. In the same way, I always think of Apple when I think of brand experiences. If you go to their office in Cupertino or you walk into an Apple store, you unbox their product, it's kind of got the same vibe.

You know, and you see that made in California, designed in California, and you're like, this is an Apple. experience I'm getting. I think you should, you know, think about your agent as a part of your brand experience. And because it can't have personality, you know, it could change per person. That's really different. You know, it's sort of the difference like Black Friday, maybe 15 years ago, everyone got the same campaign. My guess is this Black Friday, most people's incoming emails will be personalized. So we've kind of moved, you know, towards more personalized experiences. Well, agents start off with one personality and then, you know, maybe a few years from now, people have the confidence of saying let's actually reflect back the personality or demographic

of the person talking the short answer is you know we're not there to prescribe that for our customers but the fact that it's possible is really cool i mean that's just awesome and you talk about language what a remarkably empathetic thing to be able to reflect back the language of the person speaking not the language of the people you've staffed your call center with it enables something that would have been previously cost prohibitive to do something that's a remarkably empathetic And the other thing, you know, there's the delight and personality of chat, voice, video, video avatars will be mind-blowing, you know, and that's the FaceTiming with a brand. It's just like a pretty cool idea. I also think, you know, your point on, you're talking about, I think, the Klarna use case, I think we can't underestimate just how impactful this is for consumers.

The number one reason people have bad customer experiences is they had to wait, particularly in concepts of things like customer service. Like, you know, for most inbound interactions, something is not right, you know. You have a need that needs to be fulfilled. No matter how effective the person is on the other side of that email or chat or phone number, you're not going to be connected or resolved instantly. And I think this is the opportunity of AI. And it's why, you know, customer satisfaction MPS can really be driven. And it's not an indictment of the people that were doing it previously.

Those people are inherently disadvantaged by being behind. you're number 10 in line, you're on hold, right? It's a scale mismatch. By the time you're off hold, you're already not that happy. And the great people on the other side can maybe turn that around. But this opportunity is instant. And I think that's remarkable. I was trying to remind our customers, like, don't overthink this.

Like instant gratification is actually one of the main values of these systems. The rest is gravy. Yeah, even just how you think about staffing, because you can suddenly support multiple languages with a single, agent versus, you know, with a human, it's hard to know 30 languages. So even those sorts of things to your point really impact the queue and the customer. And slang and jargon and idioms, you know, I think that it's completely unreasonable to expect someone to speak 10 languages, know a term, know this. You also, there's a lot of really subtle things. So let's say your company

introduces a new product. Think of how long it would take to retrain, you know, 5,000 agents in a call center about that new product. Well, you can do that with a push of a button with AI. So there's just so many interesting, you know, second order effects of this technology that is incredibly beneficial for every consumer. So you have this amazing vantage point on the industry. You know, you were CTO of Facebook quite early on. You're a cocia of Salesforce. You're on the board of open AI. You're running Sierra. We've talked a lot about languages and applications. You mentioned sort of briefly video avatars and things like that. Are there other big technology trends that are being impacted by AI or other modalities that you're very excited about outside of the core sort of enterprise

language use cases? Or what do you think are some of these big trends that are coming? The trend that I would be really interested is what is the primary form factor by which we work with computers and software in the future? My narrative around the last not quite 20 years, but 15 years has been the smartphone has come to basically consume all, you know, adjacent technologies. I don't know if it's possible to measure, but what percentage of human computer interaction is through a touchscreen on a smartphone right now? 90%. 99%. I don't actually know. And it depends on you measure it and all that. It doesn't mean keyboard and mice win away. You know, it's for professional tasks as opposed to everyday interactions. And I think

that's really interesting. And it's been almost impossible, even for large consumer companies, to create more consumer devices that can actually reach scale because the strength of the smartphone being pretty good at a lot of things has essentially removed the market for everything else. Now that conversational interfaces work effectively, and I just think we passed that inflection boy, probably with GPT4, that's an interesting debate topic on its own,

you can speak to software networks now. So just like multi-touch meant that people could give up their BlackBerrys, does the emergence of multimodal voice-to-voice models, chat, which has already, I think, reached that point and obviously video in the future, does that mean we'll see a shift in the proverbial consumer device in our pocket in a more meaningful way? Do you have a hypothesis on form factor? Because of the perseverance of the smartphone, probably if I had to pick, I think the smartphone will remain, but coupled with things like AirPods and CarPlay and others, you'll interact with it more through different modalities. But the anchor supercomputer in your pocket probably won't go away. But I don't say like I'm hoping for it. I brought it up because how many consumer device companies have tried to build something on the side of a smartphone that was a, you know, perfect fit. And I think smartwatches maybe could qualify as a success, but still, it's not nearly the market of a smartphone. I think that's really interesting. And I also wonder, there was a, I don't know what years it was, but, you know, when everyone got

Alexis on their counters, it was, what, a decade ago? Yeah, I want to say like 2015, 16. 2015, yeah. Will those make a comeback? You know, will all of a sudden those become effective computers again? Will it make, you know, smart headphones trendy again? And then the other thing that I'm really interested in, and I don't know whether it would be optimistic or pessimistic about, which will be, will we spend less time starting at screens? Clearly, the ability of conversational AI to both speak to us, you know, through language and voice and our ability to engage with computers through language and voice, certainly theoretically it means you don't need to have the screen in front of your face all the time. Will it mean that technology recedes more into the background, or will it mean, you know, it'll just add on to everything else? I'm hopeful that product designers can take advantage of that. So a lot of the things that require us staring at our screens, the sort of like huge bag of push notifications that suck us back in, can an AI agent help us synthesize some of that so we don't end up with the reflexive response to pick it up?

That might be naively optimistic, but I'm just hopeful, like, now that we have these new ways, of engaging with computers that aren't simply through this one device and one screen, even if it is mediated by that device technically, I'm excited for that just because I do think we've sort of reached at least a local maxima of like what that experience is. And now, you know, just imagine. It's not plugged into your brain yet. Yeah, I know that could be interesting human brain interfaces. I'm very excited about the, I brought this example recently, but if you remember the first apps

in the app store, they were like flashlight and things like that, skemorphic. literal interpretations of what is this hardware capable of. And then future generations said, okay, what are the confluence of a GPS and a screen and the internet and you got really meaningful things like WhatsApp and Uber and Instacard and DoorDash? My sense is now that speaking to software has reached a, you know, sort of event horizon of effectiveness, will there be like meaningful parts of the computing experience that we depend on

that are like conversational first and will that mean that you can use it in completely different ways you know you mentioned a the device may be going away more but there's two ways of going away there's it's not front and center you're not constantly staring at it so the way you interact with it changes the second part of it is if things become very conversational or personality driven or whatever it may be what proportion of your social interactions where your day-to-day interactions shift to a computer interface versus a human yeah so if instead of chatting with a customer support wrap you're chatting with the agent there may be other like that. I'm sort of curious how you think about what proportion of human time will go to

interact with other humans versus interact with digital agents or other things over time as well. Do you view that as a trend in one direction or the other? I'll give you my what I want the world to be answered. And we can dive into cynicism if you want to. I'm hopeful in this world of AI, agents will become a meaningful part of our experience in our personal life, in our business life. With these agents, with the appropriate guardrails and safety, software can take action on our behalf. And by doing that, it enables us to not have to do those things and be present in the world that we live. And whether let's just take a Sira agent that represents a company, maybe your personal agent's chatting with it. Maybe when you're trying to figure out this problem, your agent's acting on your behalf and you can just live your life.

You know, I think the purpose of technology is to, you know, solve a problem for us. And, you know, hopefully in the world of AI and, you know, the agency afforded by AI, technology can melt away and receive in the background. There's obviously examples of people, you know, speaking with avatars. There's the, you know, things like the metaverse and all of that. And I think those are meaningful and certainly AI will change the landscape of how deep and substantive those spaces are. But I'm hopeful for most people, that's an evolution of how we think of video games and things like that. They're a meaningful form of entertainment. But you know, you can put your phone down. You can take off the VR goggles and have a conversation and have to spend less time

poking buttons on a computer to get it to do things and have your agent do it on your behalf. That's a great note to end on. Thanks to the conversation, Brad. My pleasure. Thank you. Find us on Twitter at No Pryor's Pod. our YouTube channel if you want to see our faces, follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-dashbriars.com.