No Priors Ep. 82 | With CEO of Sierra Bret Taylor

0:05 hi listeners welcome back to no priors today we have Brett Taylor whose legendary career spans from creating Google Maps to serving as the CTO Facebook and Coy of Salesforce founding two companies along the way as well as chairing the board of Twitter and now open AI he and Clay bore have started Sierra which is creating company agents for the next generation of customer experience I'm thrilled to have such an amazing technologist and leader at all scales with us today and longtime friend

0:36 welcome Brett well thanks so much for joining us today Brett my pleasure thanks for having me let's get right into it um do agents work today how do you define agents or do you want me to Define agents you define agent you're the expert agents mean something different in Academia than I think they mean in Industry right now um I think both definitions are important just starting what I view as sort of the classic academic definition is a an AG

0:59 IC 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 ink blot test for people using the word in Industry right now there's probably three categories of agent that I think are are on the cusp of working the first I think which a lot of people online talk a lot about is

1:24 personal agents and I think that's probably the earliest of the three categories that I see but but maybe one of the more exciting ones you know and this is the agent that will triage your inbox schedule a vacation um 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

1:49 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

2:18 category 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 um you know there's companies like Harvey that you know serve a legal function um there's all the coding agents and I think there's some fairly effective ones right now that serve the job of a computer programmer um I think this is really exciting because I think when you narrow I call those cases narrow but

2:45 deep if you're just trying to both task scope and U 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 of aor 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

3:12 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 Persona based agents for certain domains where the technology applies like the law um like uh areas of 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 open

3:39 Ai 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 SI Works in is what I call company agents um and it's really uh less simply about automation or autonomy but in this world of

4:04 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 um whether it's you know asking about your products and services doing Commerce doing customer service um that domain I think is shovel ready right now

4:34 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 that 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-

5:00 defined um 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 um that's just a different problem to be solved but I think you know particularly in the the areas that that Sierra works and a lot of the companies that you all have invested in is just saying you know are there some shovel ready opportunities right now with existing technology and I

5:24 absolutely think there are can you describe the um like shoveling cycle of building a company agent like what is the gap between research and reality like how do you um 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 maybe S as a starting point it may even be worth also defining like what are the products that here provides today for

5:46 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

6:03 issues with your speaker you get the dreaded flashing orange light you'll now chat with the Sonos AI which is powered by Si to help you onboard help you debug whether it's a hardware issue a Wi-Fi issue um things like that if you're a Serious XM subscriber their AI agent is named Harmony which I think is a delightful name and uh it's everything from upgrading and downgrading your subscription level to if you get a trial when you purchase a new vehicle speaking

6:28 to you about that broadly speaking I would say we help companies build branded customer facing agents um and branded is an important part of it it's 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

6:52 same novelty and probably will look back at the agents today with the same sense of oh that was quaint you know 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 a lot of the agents that customers start with are often around areas of customer service which is a really great use case but I

7:15 do truly believe if you fast forward three or four years your agent will Compass all that your your company does I used 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 uh 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

7:38 the above you know all of the above will be be done by your agent so that's what we're helping companies build and Si's initially focused on facing like um 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

8:04 that was impossible before and if you think about the typical cost of a conversation so if you call into a call center today uh 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 you know for most phone calls it's called $13 you know to to service that phone call now with AI you can bring down that cost to

8:30 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 on that like what companies would benefit most from that cost and um you know I'm not sure depending on the math equations either the numerator the denominator but if you're measuring in in millions of consumers obviously the value is is really different for Consumer companies for a lot of consumer

8:55 Brands because having conversations is a really expensive thing to do you don't necess make it easy you know um there's entire websites devoted to finding company phone numbers because often in some ways are 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 it an order of magnitude more you know what does that actually mean so

9:22 with these technology Trends I think you often start with just digitizing what you currently do but I actually think the second order fact 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

9:48 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 are quite different can you describe some of the key challenges in like taking you know the capabilities of foundation Mon and then making them work in like the company agent context one of the techniques and I think you all probably talked about on your your podcast that's very common today is it's what's called retrieval augmented

10:12 Generation Um and essentially what that means is uh you take a large language model and rather than using the model and its innate Knowledge from the pre-training process to um emit answers you combine that model with a database of content and you say use the content as a source of truth and you ask the model to summarize selected content from that that database and that's kind of a roundabout way saying if you can ground the agent and knowledge that you provide

10:40 it but also you can take off-the-shelf models and 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 uh in efficient 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

11:12 probably none of them it's all about taking action right it's upgrading or downgrading a subscription it's returning an order it's a warranty exchange it's a you know filing a claim with 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 cancelling or convince you not to

11:43 there's probably compliance goals if you imagine being a you know hippoc compliant you know Healthcare adjacent firm you 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 um we are

12:09 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 you broadly speaking most software systems for the past two decades have been rules engines that execute really quickly whether the rules are implemented a 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

12:35 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 um 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 um saying

12:59 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 in Nate and uh some of these models on the other hand if you turn that knob up to you know this is spinal tap 11 on

13:22 agency you know you could get hallucinations uh 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 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 guard rails so that you don't remove the creativity

13:50 and the agency that I think make these experiences delightful it's why chat gbt 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 guard rails 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 really a new design problem almost a a philosophical

14:16 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 where how much do you think those different aspects you mentioned the guard rails or in some cases I've seen people working on agents build their own reasoning engines and other things you know their own modules that go on top of the core Foundation models or llms how much of that do you

14:37 think as a company you need to keep doing yourself versus we 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 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

15:01 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 capex investment in Cloud I.E most software of service companies pay rent to one of those infrastructure providers like Amazon web services or Azure or Google cloud and again the because there's economies of scale and data center development it didn't make sense for start to either build their own data center or for a

15:31 startup to actually build infrastructures a service business just the capital expenditures required and that positive uh feedback loop on capex just didn't work out I think that will probably play out with the frontier models um we'll end up with a relatively small number of companies um doing pre-training uh you know which is the really Capital intensive part of of model building uh not because you know there's not they're the only places with good researchers but again if you look

15:59 at the capex requirements to actually make a return on that capex 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 is a questionable mathematically uh at least for all you think in the long run that just ends up being the main Cloud providers or Cloud providers plus one or

16:20 two other players because fundamentally toer Point there's a Capa 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 on 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 um approval budget uh security Etc so do you think it's just going to roughly consolidate around the clouds

16:43 plus or minus I do think it will roughly end up the cloud of providers in partnership with the big research Labs which is is roughly the current uh 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 um most of them use software as a service and don't necessarily care where it's hosted as long as the

17:12 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 I that all being in the same

17:38 substrate I'm not TR I make that leap I might be wrong I just you know I view the evolution of software as of 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 um you know a great one in AI might be scale AI um you know snowflake was a great example in

18:03 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 what are the tools and software that you need and then the final category would be Solutions just like in the cloud era you you can take you know the services from Amazon web

18:25 services or Azure 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 crmm or Erp system is nonsensical um and I think it you know 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 uh you know automate customer service working with

18:52 s's much easier and lower costs than building yourself if you want to you know automate parts of your legal process taring Harve is probably a much more logical path than trying to roll your own for all the same reasons it was true of software is a service so broadly speaking going back to your question you know how do you build technology and what will the foundation model providers do I think the high order bit is what is

19:15 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 I the answer is if every time there's a new release of an AI model somehow decreases your value it probably indicates you're you're not actually a solution you're you 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 not a lot of

19:39 value what happens for Sierra when models improve if we're doing our job right our platform gets better you know so you know I think that our customers you know in our platform which we call agent os are essentially defining the goals and the guard rails 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

20:04 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 um that's that's effectiv 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 a an entrepreneur I

20:25 would you know I think there's a danger if you don't fit into one of those l 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 you're in the sort of uncanny valley of of value but I do think the idea that all use cases will come from Foundation models is probably wrong I mean it's I'm

20:49 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 and I actually I actually think it perhaps the opposite came true there and you know most businesses like I was like to know like where do you

21:16 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 aart and for most companies is you know they' benefit from the rising tide lifting all boats of investing in a software as a service platform I just see the same thing happening here so I'm very bullish on uh the like going back to our

21:41 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 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

22:05 also think there's probably second order effects of relying on coding agents how they incorporate into your team governance code reviews all these things that I uh I'm not necessarily thoughtful enough to enumerate right now but that's why there's a company in this space you know and and I'm very bullish on that company existing for the long term not even knowing half the half the I think to your point the analog with SAS is a really telling one because people always

22:28 talk about rappers on Foundation models and how those companies will go away and you could argue that a lot of SAS is like a rapper on a SQL database right it's kind of like the same thing and I think the same was true probably set of Shopify Salesforce service nowly and those are all great companies you know so it's interesting they ended up being very like let's say the database vendor to Salesforce and up being a very

22:49 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 um and analogies are OB sometimes dangerous too and well let's say the exceptions to the analogy can sometimes a bit hidden but you know the foundation model providers all benefit from this investment you know these uh I really do think these Foundation models have a ton

23:12 of innate value and you know so any solution built on top the foundation model providers will collect a tax on on all those amazing use cases and that's really great for everyone involved um as you sort of alluded to you know different application companies can decide to use different models at different times creates a lot of probably healthy competition um there as well and the most important thing for say Sierra customers is we're future proofing our customers from you know

23:37 that both future proofing so you don't end up in the you know dreaded situation of something breaking when new technology comes out um 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 value they provide you know for

23:59 anyone who's experimented with prompt engineering or prompt engineering with tool use and kind of I would say the lowlevel of these models it's not like your prompt 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

24:21 companies should or will want to be operating just like you know your company doesn't want to know that you're doing a databas migration it's boring but important and you know what software as a service provides is you don't need to care about that and you 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 startups in

24:47 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 model charging for the outcome you know and I think that's a really powerful new uh business model um maybe as powerful as the idea of subscription based software and the a of software as a service that again providing out of the box solution

25:13 and aligning you know the actual business model of your company with the outcome is very meaningful and it's very meaningfully different than than paying for tokens you know and that and I think actually building that alignment with your customers is valuable um as well also commitment to that that suggests like a lot of confidence and ambition in like how valuable these Solutions can be as well it's it's non-trivial but one of the most exciting things about solution

25:37 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 I can't remember it but the pre- and post Microsoft Excel in finance departments it has to have driven just first reasoning like a slide rule versus

26:08 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 and everyone who's list incremental gains or incremental but every 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

26:34 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 guard rails we're closer to actually software actually doing a job that's quite

26:59 measurable if there's an analogy that sort of reminds me a little bit of going to from impression based ads to C per click based ads it doesn't mean you're totally towards the transaction but you're getting closer and in that transition which elot and I sort of Liv 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

27:22 direct attributions it's worth a ton and it's a great thing for companies right now that you know you should be hold lean your software you know providers to a higher standard you know and you know and not and you should get closer to the value and I actually think that's a great Trend and you know going back to our analogies of legal and coding and service like you can actually see the value it produce this this function you

27:44 know it it actually analyzed this contract you know it did this thing and you're like that I actually know how to value that like I 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

28:05 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 and you they'll have horse stories right of you know the difference between the dejection yeah the dejection and

28:30 it's complicated I think this is a really positive trend maybe a very high profile example of that um was Clara where they publicly talked about how implementing effectively customer support workflows uh for their own business I think ended up with dramatically higher net promoter 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

28:54 so it had a huge impact in terms of the how their business functioned and how they were able to deal with customers and the languages that could support 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 the impact is here and that's why I'm really excited about many of the companies sort of in

29:13 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 key 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 uh customer success I think there's a lot of effort right now ongoing as sort of sales productivity or sales and marketing productivity are

29:35 there other areas that you think are you mentioned legal there other areas that you view as sort of the most near-term next wave of these areas where you know it's very clear that these things will 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

30:01 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 uh summaries synthesis reasoning I think there's a really some interesting applications there it does seem complex you know language models aren't necessarily good at numerical or tabl data without a lot of work um domain specific data might have uh you know uh connotations or complexities

30:33 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 I I if you look at a company and a larger firm you know how many people's job it is to take data make a

30:58 presentation you know all these things 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 that's really exciting as well can we go back to um you said goals and guard rails for a minute uh like and as you described we're going away from you know complex rules engines as business software that's like a pretty big

31:24 mindset shift for your customers to ingest how do you work with them on um I guess evaluation of like how well Sierra agents work and get people comfortable with that yeah so a couple 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 um sometimes our customers come in with really well-defined processes sometimes they don't we like to say an agent's

31:52 made up of not only the factual knowledge but the procedural knowledge you know what a process follows in addition to the the the Integrations with systems and we spent a lot of time talking about where do you want guard rails where do you want creativity and where do you want agency and then we do a lot of experimentation you know uh in a proof of concept have it live and actually through sort of uh this technology and countering the cold hard

32:17 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 uh so we think that AI should not be the domain of Technology teams exclusively um 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

32:42 the steering wheel of these experiences so we built a 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 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 um there's

33:06 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 free form text box so it is completely free form so it's my going

33:28 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 to 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 uh I think it tends to there tend to be uh sort of a long taale of customer experiences that not only did we not

33:53 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 um so I think that's a really exciting Dynamic uh there's a book called The Long tale and I associate it a lot with Google I think maybe Eric Schmid wrote the forward to

34:17 it if I'm remembering 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 longtail of of blogs and you know it was really it's 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

34:44 move to a world of an AI agent um 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 things our customers want to talk to us about how do we fulfill that 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

35:07 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 it's a system and or organism that you're constantly so a lot of our platform is how do you Empower customer experience teams to manage that um and I think newed case the emerging customer Behavior not Model Behavior that's exactly right like what what new is happening in the wild today external

35:30 events controversies products that were popular that got changed you know 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 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 um scroll forward I don't know what it is six months 12 months like today we have the text box right um

36:00 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 um in each of those agents I think that your agent should be a brand ambassador what's so remarkable about large language models

36:27 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 anguage 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 um if you want a more aere brand you're like a luxury blend you can have that do you do that

36:53 as part of the prompts or do you do post trainining or how do you actually Implement that into your process 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's 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 sort of tone that sort of substance and we do a lot of what we

37:15 call supervisor models so we have models supervising other models um Turtles all the way down the our joke in our office is the solution to every problem in AI is more AI um and 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

37:38 experiences if you go to their office in copertina 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 uh experience I'm getting I think you should you know uh think about your agent as a part of your brand experience and and because it can have personality you know it could change per person

38:00 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 will 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 the personality or demographic of the person talking the

38:26 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 uh 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 enable something that would have been previously cost prohibitive to do something that's remarkably empathetic um and and the other thing you know

38:52 there's the delight and personality of of chat voice video video avatars will be mindblowing you know and that's that's the FaceTiming with a brand it's just like a pretty cool idea I also think you know your point on you were talking about I think the Clara 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 um particularly in context of things

39:18 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

39:45 it previously those people are inherently disadvantaged by being behind a your number 10 in line you're on hold right and so it's a scale mismatch by the time you're off hold you're already not that happy and and and you know the great people on the other side could maybe turn that around but this opportunity is is instant and I think that's remarkable I mean that's it's all I was try to remind our customers like don't overthink this like instant

40:10 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 some expect someone to speak 10

40:33 languages no a term know this um you also there's a lot of really subtle things so let's say your company introduces a new product um 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

40:59 so you have this amazing vantage point on the industry you know you were um CTO of Facebook quite early on you were COO of Salesforce you're on the board of opena you're running Sierra we've talked a lot about languages and the applications you mentioned sort of briefly video avatars and things like that 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

41:18 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 um I don't know if it's possible to measure but what percentage of human computer

41:47 interaction is through a touchscreen on a smartphone right now 90% 99% I don't actually know and it depends how you measure it and all that it doesn't mean keyboard and win away you know it's it's for professional tasks as opposed to Everyday interactions and I think that's really interesting and it's been uh 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

42:17 things has essentially removed the market for everything else now that conversational interfaces work effectively and I just think we passed that inflection probably with gp4 though that's an interesting debate topic on its own you can speak to software and it works now so just like multi-touch meant that people could give up their blackberries does the exist emergence of multimodal voice too models chat which has already I think reached that point in obviously video in the future does

42:47 that mean we'll we'll see a shift in in the proverbial consumer device in our pocket in 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 um but the anchor supercomputer in your pocket probably won't go away but I

43:17 don't say like I'm hoping for it you know 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 I I 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

43:38 know when everyone got alexas on their counters it was what a decade ago yeah I want to say like 2015 16 16 yeah Will those make a comeback you know will all of a sudden those become effective computers again will it make you know smart uh headphones uh trendy again and then the other that I'm really interested in and I'm um I don't know whether it' be optimistic or pessimistic about which will be will we spend less

44:03 time staring at screens clearly the ability of conversational AI to both speak to us you know through language and voice um and our ability to engage with computers through 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

44:31 can take advantage of that so a lot of the things that require us staring at our screens the you know 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

44:54 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 just imagine it's not plugged into your brain yet yeah that could be interesting human brain interfaces I'm very excited about the I I brought this example recently but if you remember the first apps in the app store they were like flashlight

45:20 and things like that skoric literal interpretations of what is this Hardware capable of and then future generation okay what are the Confluence of a GPS and a screen and this and the internet and you got really meaningful things like WhatsApp and and Uber and and instacart and door Dash 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

45:49 like conversational first and will that mean that you can use it in completely different ways you know you mentioned uh 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 or your

46:11 day-to-day interactions shift to a computer interface versus a human yeah so if instead of instead of chatting with a customer support rep you're chatting with the agent there may be other applications 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 uh agents or other things over time as well do you view that as a trend in One Direction or the

46:31 other I'll I'll give you my what I want the world to be answer and we can dive into cynicism if you want to um I'm hopeful in this world of AI agents will become a meaningful part of our experience uh in our personal life and our business life with these agents with the appropriate guard rails 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

47:01 in the world that we live and you know whether let's just take a you know a Siera agent that represents a company maybe your personal agent's chatting with it you know maybe you know when you're trying to figure out you know this problem your agents acting on your behalf and you can just live your life you know I think the purpose of Technology um is to you know solve a problem for us and and you know

47:22 hopefully in the world of AI and you know the agency afforded by AI Tech technology can melt away and Reed in the background there's obviously examples of people you know speaking with avatars there's the you know uh things like the the metaverse and all of that and I think those are meaningful and 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

47:50 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 um end on thanks for the conversation Brad my pleasure thank you find us on Twitter at no prior pod

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