Speaker 1: What's going on? Warren? How are you?
Speaker 2: You know? I was so totally unprepared for that question.
Speaker 2: I don't know what I thought you were going to
Speaker 2: say to start off the episode, but of all the
Speaker 2: things you could have picked, it was not that right.
Speaker 1: Just what a jerk asking someone how they are? That's
Speaker 1: oh man. I can't remember who it was. We had
Speaker 1: a I can't remember who the guest was, but I said,
Speaker 1: you know, there's no like stump the chump or surprise questions.
Speaker 1: And my first question what tour was how she's doing?
Speaker 1: And she's like I thought you said the would be
Speaker 1: no trick questions.
Speaker 2: That was Adriana. She was just on here, you know.
Speaker 1: Cool. So today we're gonna be talking about AI ready
Speaker 1: data and AI governance, and to help us through that conversation,
Speaker 1: we have Ina tokodev Sela from a Lumex. You know,
Speaker 1: welcome to the show.
Speaker 3: Thank you so much. Will happy to be here.
Speaker 1: I'm excited to have you here. So give us a
Speaker 1: little bit about your background and what led you to
Speaker 1: creating because you're the CEO and founder of alumin X
Speaker 1: or Alumax, So give us a little bit about how
Speaker 1: you got there.
Speaker 3: It was a long passway, but also happy about the
Speaker 3: way you know that my career took me. I started
Speaker 3: an enterprise, SAP, a huge German software company, and I
Speaker 3: think this is the you know, the most hidden choose
Speaker 3: about enterprise. You can actually have quite an adventure and
Speaker 3: then you can build stuff, big stuff right and switch
Speaker 3: careers within. So I spent twelve years at SAP, starting
Speaker 3: as an architect and then basing evolving into a customer
Speaker 3: facing role as a partner manager and then a head
Speaker 3: of pen Alpha video analytics units. So quite in the
Speaker 3: journey and quite a privilege to work with the world
Speaker 3: biggest company. Think about the worldmar builder buying and so
Speaker 3: on and so forth, so really washing companies and boarding to
Speaker 3: the cloud journey, and then machine learning and then you know,
Speaker 3: neural and augentic as well. And then I continued my
Speaker 3: career to license as business intelligence vendor and then understood
Speaker 3: what an underserved segment of business users actually are. You know,
Speaker 3: after building all these analytics for all those years, you
Speaker 3: know you're speaking to to the actual users and some
Speaker 3: of them are CEOs of the companies, and they still
Speaker 3: cannot get the hands on the actual self service analytics.
Speaker 3: So this has really moved me to build a company
Speaker 3: which creates a space where data could be recognized and
Speaker 3: meaningful semantically and business wise to the business users to
Speaker 3: enable them to self service analytics and data access. Right.
Speaker 1: And so whenever you're thinking about self service data, one
Speaker 1: of the challenges I've had in the past is self
Speaker 1: service sometimes means guiding yourself to the wrong answer. And like,
Speaker 1: one specific example I have is I was working for
Speaker 1: a company. It was like, how many how many users
Speaker 1: do we have using our app each month? It's like, oh,
Speaker 1: that seems like a pretty straightforward question, right, But turns
Speaker 1: out it wasn't because then, oh, when I meant monthly
Speaker 1: active users, I actually meant people who weren't on the
Speaker 1: trial and had converted to paid, but you know, they
Speaker 1: were like using it at least three times a week,
Speaker 1: not just once a month. And so, like a really
Speaker 1: relatively simple question turned out to be coming with a
Speaker 1: lot of constraints. So how do you deal with that
Speaker 1: in a self service world?
Speaker 3: It's perfect question for status data has to be you know,
Speaker 3: the state that you can actually create analytics on scale
Speaker 3: for many users. So when let's speak about highly curated
Speaker 3: dishboards in your intelligence tool. It's no brainer because you
Speaker 3: can sell specific data sets which you go to specific
Speaker 3: report and make sure that the hecks actually have decent quality.
Speaker 3: For companies to have self service on scales and need
Speaker 3: to make sure that any potential question could be answered
Speaker 3: with a high quality data, you first need to get
Speaker 3: your data in order to be able to provide this
Speaker 3: kind of service. And the second one is as you're
Speaker 3: right to mention single source of truth, right, So definition
Speaker 3: what active user is, so how many users do we
Speaker 3: have could be defined in dozens of different ways in organization.
Speaker 3: If you speak to someone from product departments, they will
Speaker 3: go for the active users to actually, you know, use
Speaker 3: a product. And maybe if you speak to your financial departments,
Speaker 3: it will go to someone who actually signed the contract
Speaker 3: with you. So the different ways of calculators things, and
Speaker 3: especially for self service, again you're asking about things which
Speaker 3: might have different meaning. You have to have governance actually
Speaker 3: understanding semantic business definitions of business matchakes and business terms.
Speaker 2: I feel like that's a bit of a moving target
Speaker 2: from what I've seen in my experience. Like I was
Speaker 2: remember in an organization where I'm like, we figured it out,
Speaker 2: you know, we we managed to get a single source
Speaker 2: of truth for even what a user is. Like, you know,
Speaker 2: there's a lot of identity aspects for a user, but
Speaker 2: then there's also a lot of business aspects to it,
Speaker 2: and like how great of a customer are they repeat customer,
Speaker 2: how much they've spent and then you know individual events
Speaker 2: related to it, and trying to get a single identity
Speaker 2: for that was always potentially a problem. And then you
Speaker 2: you know, every team in your organization has their own
Speaker 2: idea of what a user is and probably has their
Speaker 2: each organization has their own user management service with his
Speaker 2: own special data. And I just I wonder how possible,
Speaker 2: like if it like if there is a company out
Speaker 2: there that actually has good, good data, you know, high
Speaker 2: marks on health Scorecard for their data management, Like what
Speaker 2: does that look like? You know, is that actually do
Speaker 2: you actually see that in practice?
Speaker 3: So I would say again, because we're operating in the space,
Speaker 3: so naterally it's possible. Otherwise it wouldn't have any customers.
Speaker 1: Right.
Speaker 3: On the other hand, this is truly changed for many organizations.
Speaker 3: And I would also say that agentic practice, so generally
Speaker 3: to BI it's another silo, right, So because usually data
Speaker 3: management departments that have their own single source of truths
Speaker 3: may be implemented in the data pipelines, ls so, calculated
Speaker 3: fields and so on and so force, and then analytics
Speaker 3: department have their own single source of truth which is
Speaker 3: probably in the bi to feature store, metric store, what
Speaker 3: have you.
Speaker 2: That's an interesting point because we already see that with
Speaker 2: the public agents that are out there, they're trained up
Speaker 2: to you know, if we're lucky, even six months ago,
Speaker 2: and especially things in a business context are rolling very
Speaker 2: quickly what the principles of the organization are, or you know,
Speaker 2: what features need to be implemented for customers. Those things
Speaker 2: are iterating very quickly, and so you know, I'm know,
Speaker 2: I'm this is a new problem that I actually hadn't
Speaker 2: considered before, and it means models are fundamentally always out
Speaker 2: of date. Do the just like your documentation and our
Speaker 2: source code, right, it's all legacy as soon as it's made.
Speaker 2: Does does RAG resource augmented generation help here to reduce
Speaker 2: the changing nature of those things because you can point
Speaker 2: to potentially the production data store or is there something
Speaker 2: else going on where that realistically you're copying that data
Speaker 2: is RAGGED being used against stale data sources anyway, like
Speaker 2: you're not using the production database.
Speaker 3: It's a fair point if ROG is a separate silo
Speaker 3: in the system and you just you know, keep fitting
Speaker 3: it with examples which all of the outdated when they created.
Speaker 3: So no, RUG is not actually solving the problem. If
Speaker 3: you have started site you know as a sidecarrent data science,
Speaker 3: it will never leave up to that. But if you
Speaker 3: combine metadata management your business ontology and use it for
Speaker 3: your gentic properties, is exactly what can be up to
Speaker 3: date that point of time.
Speaker 1: So the level of overhead to pull that off seems
Speaker 1: pretty significant. Everyone feels these pains, But what at what
Speaker 1: point do you hit the scale or the quantity of
Speaker 1: data where it actually seems like a worthwhile exercise to
Speaker 1: implement this.
Speaker 3: I guess it's a healthy practice for any size of
Speaker 3: organization unless you know, we just want to rely on
Speaker 3: on the one developer who maintains your snowflake what have you,
Speaker 3: which is which is fine, okay, But but I think
Speaker 3: like Organizational Knowledge Way and its documentation was always a
Speaker 3: challenge for any size of organizations. To me, it's it's
Speaker 3: a healthy habit to actually have it from starters, to
Speaker 3: have the you know, knowledge graphts about you different data
Speaker 3: structures and the different data sources created from day one
Speaker 3: when you actually have data stores. But alway said organizations
Speaker 3: which just have one database, so one warehouse, a small one,
Speaker 3: I would not necessarily invest in that, despite the fact
Speaker 3: that I think it's not a good practice, because right
Speaker 3: now we will see flourish of agentic workflows where different
Speaker 3: agents going to communicate with each other and they have
Speaker 3: to have shared context and reasoning. And the shared context
Speaker 3: and reasoning is exactly this knowledge which you should document
Speaker 3: about your organization, right so we become more more and
Speaker 3: more automated, and we should and this is also a
Speaker 3: differentiation business differentiation between companies like if you actually advanced
Speaker 3: in keeping your knowledge and building agents around that or
Speaker 3: you're not.
Speaker 1: Do are there out of the box agents to help
Speaker 1: with this or is it completely built custom for everyone.
Speaker 3: I don't think it's feasible to really, you know, make
Speaker 3: it a max employment manual for any size of organization,
Speaker 3: because you know, data is exploding even for smaller companies.
Speaker 3: We got this approach of actual predefining ontologies for different
Speaker 3: particles and different lines of business and then automated way
Speaker 3: to simple metadata from different data sources. Don't stand with
Speaker 3: what the specific logic changes for different systems and basically
Speaker 3: automatically creates business autology. But I must say to your
Speaker 3: previous points, what we discovered as automated on boarding, which
Speaker 3: is like a few hours a few days, is that
Speaker 3: there are many proflicts of definitions. You might not aware
Speaker 3: about that now, but you have ten different views in
Speaker 3: your power bi tool where you have the same metric
Speaker 3: defined in different ways based on different data sources, and
Speaker 3: this is what we discover.
Speaker 1: Yeah, that makes sense. I can see it going horribly
Speaker 1: wrong asking your technical people about the quality of the data.
Speaker 3: We're all technical people, but I guess if you want
Speaker 3: to have a business logic, let's say, all those agents
Speaker 3: which automate customer support and so forth to be automated
Speaker 3: top your data, there should be aligned to sign organizational knowledge,
Speaker 3: which is not necessarily on technical size. It's an operational
Speaker 3: size side, so it's coming from those departments, not from
Speaker 3: us technical people.
Speaker 2: Maybe to make this a little bit more concrete, do
Speaker 2: you have like some canonical examples of what businesses are
Speaker 2: trying to often answer with the storage of their data.
Speaker 3: So I would say again, there is no business case
Speaker 3: a solid for automation as agentic as, also data compilots
Speaker 3: and ergentic and automation workflows, because if you do not
Speaker 3: inspired to automation, why would you sort out your data
Speaker 3: or why would you sort out your governance? Right if
Speaker 3: you don't have automation, like this is a killer use case.
Speaker 3: But when you have the CERO killer use case, usually
Speaker 3: companies tend to start with more like a knowledge center
Speaker 3: and discoveries so basically search right all the customer support
Speaker 3: functions on one site. On the site. We also have
Speaker 3: like companies and pharma for example Holiday building the digital
Speaker 3: health platforms with agents. We have customers and financial services industry,
Speaker 3: which which implemented self service quotation for serve party brokers.
Speaker 3: So basically is the use case would be always shortening
Speaker 3: the time and increasing conversion rates. So there are always
Speaker 3: business or metrics for implementing those use cases in the
Speaker 3: first place, but internal use cases first and then sell
Speaker 3: customer facing.
Speaker 2: And just thinking back to all the times that I
Speaker 2: was working at companies and they were saying how valuable
Speaker 2: their data would be and collected everything just waste and
Speaker 2: storage and building up internal tables of just garbage from
Speaker 2: years and years back, and realistically, I'm still waiting for
Speaker 2: the point where we could be utilizing tools to even
Speaker 2: evaluate that effectively, because is there something to this, Is
Speaker 2: there an area where it's like, oh no, actually, this
Speaker 2: probably highly useless data does turn around and solve a
Speaker 2: critical need for the company today with the advent of
Speaker 2: agents that can potentially utilize it in a much more
Speaker 2: effective way than humans can, or shortly down the road five.
Speaker 3: Ten years, Yeah, yeah, I believe so, because most of
Speaker 3: organizational data is unutilized, as I mentioned, so we see
Speaker 3: that even most advanced companies use mainly twenty percent of
Speaker 3: that data on you know, relatively frequent cadence and the
Speaker 3: res is unutilized basically, and for that data, AIO readiness
Speaker 3: also means that you actually understand few of the health
Speaker 3: core components. So startus if it's duplicated, if it's used,
Speaker 3: and if it's used by which applications, and if it's sensitive. Right,
Speaker 3: so then cent also the risk factors as well. And
Speaker 3: then a last but not least, I think it's even
Speaker 3: the most important one is what is the semantic meaning
Speaker 3: of that? What's actually hidden in this data? Right? And
Speaker 3: then that's why knowledge graphs become handy because knowledge graphs
Speaker 3: they create or suggested connections. Say, oh, did you know
Speaker 3: that additional feature of your conversion score might come from
Speaker 3: this customer demographic exparamitter like such and such, Right, so
Speaker 3: it's kind of giving you related a data which you
Speaker 3: might not encounter so far, like by your experience. But
Speaker 3: it's in there. The thing is in there right now
Speaker 3: is not covered. So companies usually do not index to
Speaker 3: catalog data, which is not used actively for applications. Again
Speaker 3: because cataloging was used for compliance and insurance sor to say,
Speaker 3: for governance, and now cataloging is small, or we should
Speaker 3: use cataloging indexing to actually for discovery. Discovery is semantic mapping,
Speaker 3: risk mapping and risk management. And of course also by definition,
Speaker 3: agents are better than humans to understand those relations on scale.
Speaker 3: I'm also like on scale, say, and then they see
Speaker 3: humans us as moderators.
Speaker 2: So you mentioned like twenty to thirty percent of the
Speaker 2: data is being utilized, So seventy percent is you know,
Speaker 2: not utilized. Is that because of the lack of value
Speaker 2: in it, lack of it being categorized effectively? Is there
Speaker 2: another bucket that I'm missing here? Something there could be
Speaker 2: something interesting in it, but no one's taking the time to
Speaker 2: actually understand it. Because I get the sense that the
Speaker 2: agents aren't going to be able to just see this
Speaker 2: uncategorized data and magically pop up an answer of how
Speaker 2: it could be valuable there still requires a human to
Speaker 2: evaluate it and know that there is something valuable in there.
Speaker 2: How it could be utilized still has to be done
Speaker 2: by a human.
Speaker 3: Yes, it's a good point. So in the databases which
Speaker 3: only have some analytics on them and so on, so
Speaker 3: first the role of the agent could be suggesting additional
Speaker 3: features to look at to take into consideration in databases
Speaker 3: where you do not have analytics at all. So, for example,
Speaker 3: we have this discussion with the company which never introduced
Speaker 3: and this is a huge company which never introduced analytics
Speaker 3: for people's departments. There are not dashboards for people's departments
Speaker 3: right now. They want to skip the stage of bi
Speaker 3: tools and go to self service data copilots to actually
Speaker 3: create this analytics to kind of scape the stage. Right,
Speaker 3: So in this case the data is supervalable. Of course,
Speaker 3: it does have to, you know, to go through automated
Speaker 3: labeling and reconciliation and semantic definitions and all of that,
Speaker 3: and you know, centrally, we have tools for that now,
Speaker 3: but here the case is you had unutilized data not
Speaker 3: for the right reasons.
Speaker 1: So do you find that after going through this process
Speaker 1: that companies actually have less data storage concerns? Because, like
Speaker 1: you know, we talk about a single source of truth
Speaker 1: and a lot of times I seen where everyone claims
Speaker 1: theirs is the single source of truth. So they want
Speaker 1: their own copy, their own database servers, their own storage
Speaker 1: system because they don't want anyone else polluting it. So
Speaker 1: after you go through this exercise, do you find that
Speaker 1: a lot of those can be decommissioned and you actually
Speaker 1: end up with less data storage overall?
Speaker 3: Just a question because single source of just has to
Speaker 3: be virtual, so it's kind of a virtual layer which
Speaker 3: connects to your operational data source, analytic data sources, and
Speaker 3: even applications because there's lots of business logic and application side,
Speaker 3: so it's always virtualized. And then the question is do
Speaker 3: you even need aggregating layers like warehouses? You know, we
Speaker 3: see this question popping up more and more and we say, okay,
Speaker 3: so there are probably going to be stages, right, so
Speaker 3: some companies are going to reduce Companies like in IoT
Speaker 3: your manufacturing space. They might want to reduce the size
Speaker 3: of the data to to some warehouse to basically have
Speaker 3: more focused use cases, right, more focused and sculpt use
Speaker 3: cases cheaper for processing. Right. And some companies who might
Speaker 3: have less data will just you know, goal result any
Speaker 3: aggregation toll. And what I'm saying less data is because
Speaker 3: the storage is not expensive anymore.
Speaker 2: I mean, I feel like there's a whole systems thinking
Speaker 2: problem here, which is just because it would be better
Speaker 2: to have a single sources. Truth, it doesn't automatically make
Speaker 2: the organizations you know, migrate to that. I do see
Speaker 2: the the XKCD article on the number of number of sources.
Speaker 2: I mean it says standards, right, you know, we have
Speaker 2: three three databases with user identity, user tracking metrics data
Speaker 2: in it, and oh we should have one you know,
Speaker 2: unified answer, one perfect database that is sanitized, that is
Speaker 2: categorized correctly. And the result is now we have four
Speaker 2: user databases, you know, with all the data in it,
Speaker 2: and and you know, someone still utilizing those old ones
Speaker 2: and to your point of storage still getting cheaper for us.
Speaker 2: There is no justifier, and it takes effort, you know,
Speaker 2: human time and resources to actually decommission a database. I
Speaker 2: can see that just not being encouraged to even happen.
Speaker 2: What if there's some something we missed in there that's
Speaker 2: still valuable that we could be utilizing to increase our
Speaker 2: business even by you know, a couple of percentage points.
Speaker 3: So I guess it's a virtualized ontology which lies knowledge
Speaker 3: craft which you can connect to many data sources and
Speaker 3: indicate which data source and which table call you need
Speaker 3: to use for specific vision. To me, is something that
Speaker 3: can with the time help you to decommission specific data source.
Speaker 3: All in migrating you know, systems to new storages. And
Speaker 3: I heard this talk at Gardner last year when someone
Speaker 3: was comparing hadup to data lakes or data lake houses
Speaker 3: and all of that. Because if you do not have,
Speaker 3: like the semantic layer, the business understanding of what's in it,
Speaker 3: this big store of data doesn't really solve your problem.
Speaker 1: So what's the biggest driver for this is it does
Speaker 1: it typically come to you from the business side or
Speaker 1: from the technical side customer?
Speaker 3: Yeah, I think it's good news for the whole industry
Speaker 3: that everything about Argentic is coming from the business side.
Speaker 3: And yeah, it's a good position to be in because
Speaker 3: this is where money is, right, it's where decision power
Speaker 3: is and so on the first and you don't need
Speaker 3: to explain the technology anymore, right, you don't need to
Speaker 3: explain yourself anymore, because you know, I think it's the
Speaker 3: same that what happened with the Internet, and you know
Speaker 3: early two thousands with the dot com boom, that the
Speaker 3: business side were like super inspired to create e commerce
Speaker 3: use cases and what have you. And that's what's happening
Speaker 3: with Argentic. It's business side. It's already you know, also
Speaker 3: inspire art with all the capabilities of this new technologies
Speaker 3: that are actually inventing the use cases and the building
Speaker 3: you know, business drivers and calculations behind that. This was
Speaker 3: usually the prerogative of technical teams, right to come up
Speaker 3: with a new technology and then find a compelling business case,
Speaker 3: and now it's the other way wrong. On the other hand,
Speaker 3: business so technical teams are struggling on the site to
Speaker 3: provide this type of service that the business as far
Speaker 3: as to because of law data quality, because of low
Speaker 3: data readiness, and because of this multiple definitions where if
Speaker 3: you connect agents to them, you know it's it's a business.
Speaker 2: Sir, I mean, it really seems like there's the innovation
Speaker 2: Here is a fundamental paradigm shift from having business intelligence
Speaker 2: and even data centered engineers working within organizations to completely
Speaker 2: outsource the handling of any sort of data from your
Speaker 2: production systems, because at the end of the day, they
Speaker 2: were always sort of a bottleneck for delivering things that
Speaker 2: used to be someone's like I need a dashboard for this,
Speaker 2: or being able to answer the question of how we
Speaker 2: talk about monthly active users, Well, where is that data?
Speaker 2: What does it look like, and then figure out utilizing
Speaker 2: the tools to actually build the dashboards. Having the data
Speaker 2: in a single place all that had to be solved,
Speaker 2: whereas now those teams don't necessarily need to be working
Speaker 2: on that anymore. The data starts in the original application.
Speaker 2: You don't want a middle layer. You want it given
Speaker 2: to companies that understand how to sanitize it, what's relevant,
Speaker 2: where the insights are, and providing an interface for those
Speaker 2: asking the questions to directly interact with the data in
Speaker 2: an understandable way, rather than looking at dashboards that are
Speaker 2: out of date or configure some ultimately utilizing tools that
Speaker 2: just don't really work that well because there's there's too
Speaker 2: many degrees of freedom, too many variables, too many columns
Speaker 2: or pieces of data that all needs to be displayed
Speaker 2: depending on what you're actually looking for.
Speaker 3: Yeah, it's a good point because a majority of foul
Speaker 3: decisions are ed HOLC decisions on aed hoc questions. I
Speaker 3: do believe that we'll still have space for KPIs and dashboards,
Speaker 3: Like I am starting my day with you know, Google
Speaker 3: Analytics and Salesforce and all of that, but I also
Speaker 3: have like a bunch of questions which are not in
Speaker 3: those reports, and it is actually changed with the data
Speaker 3: change every day, right, And I would like to have
Speaker 3: a tool which can help me with that. And for that,
Speaker 3: I think that we could be as practitioners, like as
Speaker 3: analytics practitioners, we could be smart about it because if
Speaker 3: you actually get monitoring and agentic metadata analytics, you actually
Speaker 3: understand what are the interactions of users with the systems,
Speaker 3: you will come up with dashboards which are actually useful,
Speaker 3: right because the biggest criticism, well from the end user
Speaker 3: that you build like a bunch of dashboards that we
Speaker 3: didn't ask for. And now when they have this luxury
Speaker 3: of asking that questions freely, you can monitor what's asking about, right,
Speaker 3: ask asking for and actually create a takes is actually
Speaker 3: neat and you know, convert into the dashboards.
Speaker 2: Now, I'm definitely more on the anti dashboard proponent or
Speaker 2: I guess dashboard antagonist, Like I find there's something very
Speaker 2: like you're utilizing as a crutch and maybe a little
Speaker 2: bit lazy as far as not articulating what the challenges
Speaker 2: or the question you want answered. It's like, oh, I'll
Speaker 2: just look at a dashboard of this information, maybe the
Speaker 2: answer will pop out where what you really want to
Speaker 2: do is say why am I looking at the dashboard?
Speaker 2: What am I really looking for? And have the answer
Speaker 2: to your question. You don't care that the number of
Speaker 2: monthly active users that is increasing over time, but maybe
Speaker 2: you're utilizing that to figure out, well, where are the
Speaker 2: biggest jumps? Why was there a jump here and there?
Speaker 2: And so the question you want to ask is where
Speaker 2: are the biggest jumps and what happened to my organization
Speaker 2: or to our customers or competitors or in the global
Speaker 2: market that caused the biggest change in the last six months.
Speaker 2: And rather than looking at a graph that says that
Speaker 2: basically underlying data, you're getting the answer straight away, and
Speaker 2: then you can actually take the next step. And I
Speaker 2: guess one of the reasons I'm such a dashboard antagonist,
Speaker 2: I just coined that to our term right now is
Speaker 2: I mean, we focus a lot on high availability systems
Speaker 2: and high reliability. You can't know that you're you can't
Speaker 2: rely on a dashboard for telling you if your your
Speaker 2: system is up or down. Like you need to know
Speaker 2: deterministically what the answer is at any single moment. And
Speaker 2: I think the only difference is from a business is
Speaker 2: it's more long term. Although I think a lot of
Speaker 2: companies delude themselves into thinking that it can be a
Speaker 2: short term answer, that I can just look at this
Speaker 2: right now and automatically know what my next step is
Speaker 2: that I should take. And it's a lot more I
Speaker 2: feel like deep dive into really understanding the correlations between
Speaker 2: the underlying data stores.
Speaker 3: Yeah, I think dashboards is the way that you can
Speaker 3: tell the story like in coherent way, like from usually
Speaker 3: from the experience, right, and when you just asking questions,
Speaker 3: you know, using your slack teams, it could be random
Speaker 3: questions without the context and without continuation. It would be
Speaker 3: like just party questions. Right. So in those words, usually
Speaker 3: when the builds right, it's the best ones, right, not
Speaker 3: all of them. You actually have a phenomenon, right, and
Speaker 3: measure and then a bunch of which is that explain
Speaker 3: like where it's coming from, like segmentation and audiences and
Speaker 3: so so so. So basically it's a good way to
Speaker 3: visualize changes. But again, this is the way that also
Speaker 3: doesn't allow you to recognize new things. It might be
Speaker 3: a new factor that affecting what's in your dashboard, and
Speaker 3: you will never know that because it's not automatically recognized
Speaker 3: and you're not popping up. Whereas when you use authentic
Speaker 3: you can know, okay, what are all the factors with
Speaker 3: influencing this spike, and then you will actually see like
Speaker 3: what is the related features which can affect right, So yeah,
Speaker 3: yeah to your point, to me, this world is a
Speaker 3: good starting point. It's not an endpoint, but a starting
Speaker 3: point where it can actually start your exploration going further.
Speaker 3: Everything is going to be data driven, and you know
Speaker 3: you don't have to have like myself, fifty different types
Speaker 3: open in your browser and this might affecting this upload speed.
Speaker 2: Yeah, just fifteen.
Speaker 1: Those are rookie numbers.
Speaker 3: Yeah, so so I guess we'll not have to use
Speaker 3: so many applications for ever saying and you know, learning
Speaker 3: all those applications. It's it's about experience. I have a question,
Speaker 3: I have a task. I want to complete that, and
Speaker 3: I don't really care which applications and data is involved.
Speaker 2: Right, it's so so optimistic.
Speaker 3: You don't think five years is realistic.
Speaker 2: I think that because of competition, there are always and
Speaker 2: the segmenting of data, availability of data in the market,
Speaker 2: Like there's no public Internet anymore, We've already seen the
Speaker 2: closing off of available data sources. Every single one of
Speaker 2: these applications is going to have access to just smaller
Speaker 2: pieces of data that are more focused, and you're still
Speaker 2: going to have to go from app to app to
Speaker 2: get these questions answered. And I think some companies are
Speaker 2: trying to push forward some way of still having like
Speaker 2: a single pane of glass to interact through utilizing MCP,
Speaker 2: the Model Context Protocol or a A two a agent
Speaker 2: to agent. Thank you Google for you know, coming up
Speaker 2: with something different. And you know even that, you know
Speaker 2: we can't even standardize you know, a single you know,
Speaker 2: paradigm for a protocol to communicate between agents. So you know,
Speaker 2: I don't I think we failed up to this point,
Speaker 2: you know, in the air twenty twenty five for humans
Speaker 2: to you know, have one agreed upon answer. I just
Speaker 2: don't see it happening unless you know, fundamentally your daily
Speaker 2: driver changes. And I know on the software engineering side
Speaker 2: we try to make it be the ide of choice,
Speaker 2: but even that still, like, I don't think everyone is
Speaker 2: spending all of their time just in that one tool.
Speaker 2: You're still switching back and forth to different communication tools
Speaker 2: and whatnot.
Speaker 1: No, No, I'm just thinking like everyone's in favor of
Speaker 1: a single plane of glass as long as I'm the
Speaker 1: provider of a single pane of glass.
Speaker 2: I think that sort of maybe brings in a question
Speaker 2: the Risondet like of the existence of the company, like
Speaker 2: what what are they doing that is fundamental? Like what
Speaker 2: is it that they're really trying to sell? And I
Speaker 2: feel like a lot of companies out there they just
Speaker 2: copy each other, like they're not creating something unique there.
Speaker 2: So I still see there always being an opportunity to
Speaker 2: own the data and sell it. And I think maybe
Speaker 2: this goes back to the question of if you're not
Speaker 2: providing something unique, then other companies can spin up and
Speaker 2: still own the data, and why not you pay some
Speaker 2: company to provide you with the answers to questions and
Speaker 2: manage all the data. And I think this has been
Speaker 2: a model that has existed in certainly with like a
Speaker 2: user research groups for instance, think tanks, consulting companies that
Speaker 2: come and tell you how to just do your business
Speaker 2: exactly what the data should be in everything. So I
Speaker 2: don't know, like even if in your own company you
Speaker 2: have a lot of data and you're like, we know
Speaker 2: how to utilize the data most effectively. We can go
Speaker 2: and hire an engineering team to go create that single
Speaker 2: pane of glass. Eventually you're like, well, other companies can
Speaker 2: use that pane of glass too, We'll start selling it.
Speaker 2: And then that company becomes just the seller of a
Speaker 2: pane of glass. So you know that's It's like I
Speaker 2: was just going to keep on going, but it really
Speaker 2: does bring up to the point where if another company
Speaker 2: can answer all of your business questions for you, oh
Speaker 2: what is there left to still be able to do uniquely?
Speaker 3: So every organization has the old prietary data based on
Speaker 3: the nature of business and the customer base, and even
Speaker 3: someone comes and says, okay, right now, we are going
Speaker 3: to bring you into standards about how to make business.
Speaker 3: You'll still customize it what they only have like this
Speaker 3: is your advantage on one side. On the other side, yeah,
Speaker 3: if you have very special data and you want to
Speaker 3: sell it, you might want to sell it in machine
Speaker 3: readable format which is not reverse engineered. Right, So you
Speaker 3: do not sell data buy tables like they kills, right,
Speaker 3: but you sell your data as an en semantic meeting
Speaker 3: so so basically as machine readable format, so other algorithmsave
Speaker 3: agents can use it, but they cannot decipher that, right.
Speaker 3: I think it's actually the most secure way right to
Speaker 3: share data for specific use.
Speaker 1: Speaking of which, what are the security concerns that you
Speaker 1: deal with whenever you have an agent that has access
Speaker 1: to all of these different data sources.
Speaker 3: So in our organization, we actually choose to separate data
Speaker 3: values from data concepts from agents, so agents only have
Speaker 3: access to data concepts and then when the care is generated,
Speaker 3: it runs in separate environment on the data values and
Speaker 3: ELMACS does not ever touch data values of our customers
Speaker 3: just displayed in the customers applications, So we have total
Speaker 3: separation between agents and the data values themselves. So this
Speaker 3: approach actually allows you not to be concerned about the
Speaker 3: data leakage on a thing like that. I think every
Speaker 3: company will decide for themselves this you know on premisey
Speaker 3: deployment is right. I would never actually vote for that
Speaker 3: because models investings so fast and you have limited capability
Speaker 3: to upgrade them if you go for the own premisey
Speaker 3: deployment rather than just using APIs which always go forwards
Speaker 3: and so on. So firce. But you know, everyone will
Speaker 3: will make the choices again based on sensitivity and bright
Speaker 3: in nature of the data is probably going to be
Speaker 3: leveled some way that we have a you know, cloud
Speaker 3: storage with on premise and you know, different governance practices.
Speaker 3: It's going to to be the same for Augentic. Right now,
Speaker 3: it's more and modransparency about what's moving there, and the
Speaker 3: more punish about from the company side, like what's critical
Speaker 3: and what's sensitive for them to basically send to serve
Speaker 3: parties or what could be kept inside.
Speaker 2: I really, I really like that perspective. If it was
Speaker 2: ever true in the past, where you could be profitable
Speaker 2: with an on prem data center storing all your data
Speaker 2: and running all your compute, that must be less and
Speaker 2: less true every day, and you'd have to be doing
Speaker 2: something very special for you to find value in that
Speaker 2: because technology is iterating even faster now, right like any
Speaker 2: argument you would have had in the past is now
Speaker 2: no longer valuable. And so like I'm totally with you.
Speaker 2: I don't I don't understand even ten years ago how
Speaker 2: people were justifying on prem solutions and now it's like
Speaker 2: even even less of the case.
Speaker 3: It's cost prohibitive to move data to AI. You need
Speaker 3: to bring AI to data and if your data is
Speaker 3: on premise, it means you need to bring a I
Speaker 3: on premise. And this is like, you know, complicated and
Speaker 3: not very efficient to deal with things. But you know,
Speaker 3: you make your decision by some risk management. I guess
Speaker 3: the aw US.
Speaker 2: Has the snowmobile, uh that you you know, just transfer
Speaker 2: the you know, get the USB sticks on a giant
Speaker 2: truck and you know, fly to the data center. And
Speaker 2: you know, I think I think that can work out.
Speaker 2: I mean, I think if anything, now, it's less about
Speaker 2: like it must be less about the amount of data
Speaker 2: you have and the rate of data creation. And I
Speaker 2: can see that with the number in a manufacturing plant
Speaker 2: or in healthcare, like the number of sensors increasing every
Speaker 2: all the time. You know, I'm wearing one here and
Speaker 2: I'm thinking about getting another one, and they're just going
Speaker 2: to increase more and more, and so with that increase
Speaker 2: you need to be able to handle it much more effectively.
Speaker 2: I think storage costs coming down at the cloud providers
Speaker 2: is probably the next innovation that will happen there. We
Speaker 2: just saw aws's as three one zone drastically reduced by
Speaker 2: like eighty five percent costs there, and I think we'll
Speaker 2: continue to see that as storage costs decrease over time,
Speaker 2: so it would just become more and more feasible to
Speaker 2: put data in the cloud closer to the agents.
Speaker 3: Yeah, processing is always a bigger concerns. That's storage for
Speaker 3: many many years now. And I think this is also
Speaker 3: something that many of us in the news line what
Speaker 3: it was like last months, two months ago about deep six,
Speaker 3: So how much ability costs to actually train model. We
Speaker 3: actually have a in inference running, So the costs of
Speaker 3: processing is shifting from training to use of the models
Speaker 3: to the inference itself, and that's where I see that
Speaker 3: the majority of funds is going to be spent actually
Speaker 3: using AGENTIC on the data. And again, if it's more
Speaker 3: efficient on the cloud on data centers, I would say
Speaker 3: it's going to be more efficient in the cloud because
Speaker 3: especially if you're not locked into specific provider, is going
Speaker 3: to be more and more competition than that, and especially
Speaker 3: when you can recognize what data is garbage and what's
Speaker 3: not and kind of limit the footprints, so everything, all
Speaker 3: the costs are going to be down a thing. Right now,
Speaker 3: we spent lots of money already on the data pipelines
Speaker 3: which are duplicated to each other and not always feeding
Speaker 3: information that we actually use an application. But because companies
Speaker 3: do not monitor the metadata, they don't know what's in
Speaker 3: use and what's not right, so we already have like
Speaker 3: to spend which is pretty defined, and you paid anyhow
Speaker 3: if you use your dish verse, if you use applications,
Speaker 3: So if you're not, you're still paying the data vibelines
Speaker 3: that you have. So gente tick might replace as habits
Speaker 3: by actually invoking information and processing that you use and
Speaker 3: not which is pretty defined for you by someone assumption.
Speaker 1: This is probably an unpopular opinion, but I think we're
Speaker 1: going to look back decades from now and say that
Speaker 1: making storage costs so inexpensive was the worst mistake we
Speaker 1: ever made.
Speaker 2: I mean, as Gavin's paradox, right. I mean, anything anything
Speaker 2: that we don't want to have, we should not make
Speaker 2: more efficient because we will eventually over overutilize that thing. Yeah,
Speaker 2: I mean that it happened in I think really the
Speaker 2: industrial age, in especially England with coal mining. Yeah, I mean,
Speaker 2: for sure people are are utilizing or systems and it's
Speaker 2: abusing ways. Cloud providers have to have a strategy for
Speaker 2: dynamically swapping out hard drives as they fail because we
Speaker 2: haven't improved the reliability of them, just.
Speaker 1: Just the size.
Speaker 2: Yeah. Right, and you know that's sort of a problem.
Speaker 2: I mean, I think it's a science fiction ideal that
Speaker 2: we figure out how to inscribe and write and utilize
Speaker 2: data and sort of like a pure energy e lertromagnetic
Speaker 2: you know, constrained field inside like diamonds or something. I mean,
Speaker 2: it'd be nice, honestly. Well you're going to start working
Speaker 2: on that, Yeah?
Speaker 1: Probably not.
Speaker 2: Where are the customers?
Speaker 3: Where are the customers? Well, my next gig is going
Speaker 3: to be in logivity for sure. I think it's fascinating fields,
Speaker 3: and I think we have one more data to actually
Speaker 3: have a breastrow sense fields. But yeah, yeah, I think
Speaker 3: data volumes are not necessarily a bad thing. But it's
Speaker 3: not about data ballis. It's about data variety. I wouldn't
Speaker 3: say like, actually have big data is advantage, but actually
Speaker 3: have rich data. Right.
Speaker 2: So that's a really good point actually that I don't
Speaker 2: think anyone's brought up on the show before. I actually
Speaker 2: have a colleague that looked into the connections between networks
Speaker 2: human networks. But I think it applies here that as
Speaker 2: you said, it's not about the volume the amount that
Speaker 2: you have, but there's some arbitrary aspect of the of
Speaker 2: the data that's like super critical here, which is the
Speaker 2: say connectivity, but also the sparseness of it. How how,
Speaker 2: I don't think it's a metric for that for for
Speaker 2: what that is. Maybe maybe you're calling it something special.
Speaker 3: We call it interoperability, maybe not the best word for
Speaker 3: that Internet, but it means it's actually like for data
Speaker 3: assets like table you can have different types of analysis
Speaker 3: or for analysis you can use like different assets to
Speaker 3: fit it in. So interoperability is the ability to to
Speaker 3: match different features between different sources, and that is a complimentary.
Speaker 2: Yeah. So okay, so I have to ask about this.
Speaker 2: Some of the marketing for your company says that you
Speaker 2: don't have any hallucinations, and so we know that hallucinations
Speaker 2: are coupled to utilizing a straight transformer architecture. You know,
Speaker 2: if you're using transfer architecture, you must have hallucinations. So
Speaker 2: you must be doing something special that other companies aren't utilizing,
Speaker 2: you know, different from what the lms are are building.
Speaker 2: Is that something you can talk about?
Speaker 3: Yeah? Sure. So our approach is to ground a single
Speaker 3: source of choice in your knowledge graph, right in your
Speaker 3: business intology, which is transparent right as businesstology is represented
Speaker 3: this knowledge graph of semantic embeddings. So for starters, we
Speaker 3: allly have kind of organizational agreements on what business logic is. Yeah,
Speaker 3: and in addition to that, we actually ground your experience
Speaker 3: only to the business ontology, right, So we degree we
Speaker 3: reduce the degrees of freedom of the model not to
Speaker 3: to think you know widely about universe, but to think
Speaker 3: about your companies in the universe. So when you ask
Speaker 3: a questions about active users, it will not think about
Speaker 3: Wikipedia definition of active users, will think about your business
Speaker 3: matching definition of active user, maybe if coming from your
Speaker 3: bi tool, right, So it's really grounding the experience of
Speaker 3: the users in the single source of choice of your organization.
Speaker 3: In addition, because we always build the synthologies based on
Speaker 3: the metadata, we understand the context much better. But do
Speaker 3: not only understand the context of user interaction within specific
Speaker 3: memory a frame in the copilot. We also understand the
Speaker 3: user interactions with any system which is connected to a
Speaker 3: remax right, so it's basically previous interactions with operational systems,
Speaker 3: with analytics systems. So our context is much wider and
Speaker 3: we can have much more personalized experience for the user
Speaker 3: based on this metadata access. And the third reason is
Speaker 3: because okay, so the first reason was the business ontology
Speaker 3: single sos of truths grounding for experience. The second one
Speaker 3: is personalization, and the third one because we do have
Speaker 3: business atologies which are a complimentary like in the language
Speaker 3: and so on and so forth before the customization for
Speaker 3: specific company. When users use different language which is different
Speaker 3: from the business metrics definitions and the organization, we can
Speaker 3: pick it up from our you know, generic contologies about
Speaker 3: this vertical because people switch companies, they might use different lingos,
Speaker 3: different abbreviations sure which are not necessarily implemented in this company.
Speaker 3: And we have not only user context, but we also
Speaker 3: have industry contexts so we can pick up this language.
Speaker 3: So those three reasons allow us to have much reduced
Speaker 3: experience on one side. On the other side, it's a
Speaker 3: very very first lace, so you cannot ask. It makes
Speaker 3: about whether you can only ask it makes about you
Speaker 3: connected data, so you're.
Speaker 2: Not utilizing as much of a probabilistic model as other
Speaker 2: companies that have built their own foundational models.
Speaker 3: We haven't built foundational models, but we use dozens of
Speaker 3: semantic models and two dozen of graph models for different
Speaker 3: tasks from onboarding to the user experience and explainability to
Speaker 3: provide this type of experience, and we always keep an
Speaker 3: eye on the latest and greatest, so we also when
Speaker 3: the new models come out, we test them and see
Speaker 3: how we can embed it and now andsemble and it
Speaker 3: helps us to you know, increase accuracy over time. But
Speaker 3: I think the biggest thing is if we give the
Speaker 3: ownership on the context and reasoning for the organization that
Speaker 3: we serve, we automatically build it for them and from
Speaker 3: now they are the owners of the context and reasoning.
Speaker 3: And if they want to plug tomorrow and Vida names
Speaker 3: so able as bad Shock, they can use this contexts.
Speaker 3: So it's kind of feeling, it's transparent and it's usable.
Speaker 3: So for us, this is the biggest benefit.
Speaker 2: Actually, so there's still there's still a chance that it
Speaker 2: will hallucinate. It's just very very low and it will
Speaker 2: stay within the in the context of the business domain.
Speaker 1: No, no, it's not a hallucination. It's a guide spiritual journey.
Speaker 3: You mean, if you do have many versions of truth.
Speaker 3: So for example, Janna is just introduced new definition of
Speaker 3: active user and new dishboard. It makes fixed up and
Speaker 3: if someone asks about douctive user, we might offer like, okay,
Speaker 3: there is new definition in UBI dishboard. Would you like
Speaker 3: to get answer on that.
Speaker 2: Wells, as long as there's a probability of how you
Speaker 2: generate a solution the answer, there's always a chance for
Speaker 2: it to pick it just make something up, even if
Speaker 2: you have tried to constrain it by actual definitions. Otherwise,
Speaker 2: that's just a fundamental aspect of probabilities. So I mean,
Speaker 2: while you can definitely reduce it and eliminate duplicate definitions,
Speaker 2: there's a whole other part of the transform architecture which
Speaker 2: fundamentally requires the creation of pollutionination. It's like, I know,
Speaker 2: you can have a transform architecture without that.
Speaker 3: Again, it's a good point, and we provide explainability about answers,
Speaker 3: so it's not likely asking question for U and have
Speaker 3: numbers and answers actually provide folks mobility Like this is
Speaker 3: how we understand the questions. This is a semantic entity
Speaker 3: that we met this question too, and this is logic
Speaker 3: and all of that. And if user would like to
Speaker 3: to base the answer on different logic as I can
Speaker 3: actually choose like this not autopilot mode. And see, okay,
Speaker 3: this is the related semantic entities to a question. You know,
Speaker 3: you can pick up from them if you'd like to. Really,
Speaker 3: like my husband, he drives alpham Mito manual stick right,
Speaker 3: so he will always prefer to to have better control.
Speaker 3: It just back from Italy. So it's like those are
Speaker 3: also created for manual driving. So it's like some data
Speaker 3: is created for manual selection. Probably right, If it's like
Speaker 3: super A, I see you might want to select it manually,
Speaker 3: I would say, lucky. We will. Of course as an industry,
Speaker 3: we're going to be more and more automated. You know,
Speaker 3: some people just like more control.
Speaker 2: I think it's like the the idea of control more
Speaker 2: so than actually in control, like you know, you don't
Speaker 2: want you don't want, you don't want the manual stick shift.
Speaker 2: You want to be told it's a manual stick shift.
Speaker 2: But if you mess up and do the wrong thing,
Speaker 2: the right thing is still happen.
Speaker 3: That's choose a choose that always, you know. Yeah, we
Speaker 3: have systems like aybeas and all that to keep us safe.
Speaker 3: That's true.
Speaker 1: Do you want to shift the gears but you don't
Speaker 1: want to dump the clutch?
Speaker 3: Probably awesome, not over like comel like you know, two
Speaker 3: hundred meters above the water.
Speaker 1: And no, right, not really awesome. So it feels like
Speaker 1: this might be a good place to roll into picks.
Speaker 1: What do you think? Okay, let's do it, Warren. Yeah,
Speaker 1: you're never gonna guess what's happening next.
Speaker 2: Okay, I'm going first. Yeah, so I got a really
Speaker 2: controversial good one here. There's yeah like like, I like,
Speaker 2: I like it. So there's this great article that I
Speaker 2: read through. It's short, it's short form, so it should
Speaker 2: be easy for anyone to get through. It's basically the
Speaker 2: idea of how intuition is being used in software engineering
Speaker 2: and whether or not lms are capable of intuition and
Speaker 2: It is actually a proof that shows we can't have
Speaker 2: AGI with transform architecture. Our lms will never be able
Speaker 2: to reason. And it utilizes Google's incompletely its theorem the
Speaker 2: non computability of intuition and the computability of touring machines,
Speaker 2: and just with that we can actually prove fundamentally that
Speaker 2: we can have AGI with our current systems. We haven't
Speaker 2: gotten any closer to that. So don't don't listen to
Speaker 2: the lies that people have been sharing from massive quote
Speaker 2: unquote AI companies, because the real argument here is that
Speaker 2: in order for us to have a GI, you need
Speaker 2: to introduce intuition, and that's the exact thing that's lacking
Speaker 2: in turning machines.
Speaker 3: Question too, are you born?
Speaker 2: Yeah, well there there is the.
Speaker 3: Just answer that if you're not born.
Speaker 1: Answer the question Warren, Yeah.
Speaker 4: It means experience really so yeah, yeah, I mean it's
Speaker 4: really hard to identify even what happens as an individual,
Speaker 4: let alone if we can believe it on an external system.
Speaker 2: Luckily, the Turing Turing machines we know, you know are
Speaker 2: are are our closed systems.
Speaker 3: There.
Speaker 2: I I likened it to this great quote which will
Speaker 2: be a future pick of in a in a future episode.
Speaker 2: A parrot reciting Shakespeare. That's that's l MS today. And
Speaker 2: you would never claim that that a parrot, you know,
Speaker 2: would fully understand you know what it's reciting there. Uh,
Speaker 2: And that's that's unfortunately the extent of our technology. That's
Speaker 2: my pick right.
Speaker 1: Ina, you're upe, what did you bring for a pick.
Speaker 3: Per pack? Well, I wasn't for that, but I must
Speaker 3: say yeah, So let's speak about AGI as well. I
Speaker 3: do not believe in AGI in the next five to
Speaker 3: ten years, at least, just the fact that these humans
Speaker 3: be capable of applying context from one experience to different experience.
Speaker 3: So I do not call it intuition because intuition, to
Speaker 3: me is just experience. But our ability to merge contexts
Speaker 3: which are vividly not connected is whereas the humans park
Speaker 3: is and to me, AGI is not going to be
Speaker 3: near that in the overseable future, let's say ten years.
Speaker 3: So think about you can apply your knowledge from cooking
Speaker 3: to your knowledge of right now, you know, coding or
Speaker 3: something like that, like what the ingredients and so on
Speaker 3: and so first, so our associations work differently than machine association,
Speaker 3: and this context merge from unrelated experiences something that machines
Speaker 3: are not good with.
Speaker 2: I like how you went to the philosophy side of this,
Speaker 2: you know, our you know, there's this idea that the
Speaker 2: universe is deterministic and that everything is connected through the
Speaker 2: collapse of the you know, Shirtinger's equation, the wave function,
Speaker 2: and uh.
Speaker 3: It's called religion. Yeah.
Speaker 2: Oh I was ready to go there.
Speaker 3: Yeah, yeah, I do.
Speaker 2: I do agree. You know, fundamentally, there's something missing from
Speaker 2: the computing systems that we build today in order to
Speaker 2: actually achieve AGI.
Speaker 1: My pick's going to take this down a whole big
Speaker 1: notch because you know, we were talking about having the
Speaker 1: number of the number of tabs that you have open
Speaker 1: in your browser. For the last couple of months, I've
Speaker 1: been using the ARC browser and specifically that conversation. One
Speaker 1: of the things ARC does is any tab that you
Speaker 1: haven't touched in the last thirty days, it just closes.
Speaker 3: It for you.
Speaker 1: And I used to have a bunch of tabs open,
Speaker 1: and I was like, Okay, I'm going to try this.
Speaker 1: I'm gonna hate it. I'm going to figure out how
Speaker 1: to turn that feature off, or I'm going to quit
Speaker 1: using it. After several months it's closed. Probably hundreds of
Speaker 1: t as for me and I've not noticed. So go
Speaker 1: try out the ARC browser. You don't need all those tabs.
Speaker 2: I think I'm having a little bit of a neurological
Speaker 2: meltdown just at hearing about that feature.
Speaker 1: Well, right, it's panic inducing, Yeah, for sure, definitely.
Speaker 2: I mean there are tabs that I actually leave there,
Speaker 2: that I know are there and I don't want them
Speaker 2: to go away.
Speaker 1: Cool. I have a follow up question for you, Warren.
Speaker 1: Though you mentioned that you wear you're currently wearing one
Speaker 1: IoT device and you're thinking about getting another one. What
Speaker 1: are you wearing and what are you thinking about getting?
Speaker 2: Yeah, so this isn't my pick, but I'm wearing the
Speaker 2: Google pixel Watch too. I would definitely not recommend anyone
Speaker 2: to get a smart watch ever. So that's one. So
Speaker 2: I want to replacement. This thing disturbs me while I'm
Speaker 2: sleeping and I would really like to get my sleep metrics,
Speaker 2: and so I've been looking at alternatives there. I the
Speaker 2: number one one is the ORR ring. I think they're
Speaker 2: on edition four, but it's a subscription based thing and
Speaker 2: that really rose me the wrong way. So I'm not
Speaker 2: I'm not interested in that realistically. But that's why I'm
Speaker 2: hoping for a good ring without a subscription to show
Speaker 2: up that I think I'd be okay wearing a bed.
Speaker 1: No, I was just curious, curious I have for my watch.
Speaker 1: I have a Garment Phoenix, which is technically a smart watch,
Speaker 1: but I have everything turned off on it. The only
Speaker 1: thing I use it for is heart rate and uh
Speaker 1: metrics whenever I'm out for a run. And I had
Speaker 1: an or a ring for a while and same same
Speaker 1: with you, is like, I don't want to pay a
Speaker 1: subscription for it. And I know a lot of people
Speaker 1: who use the Whoop Band, but it's another subscription based service.
Speaker 1: But they're like, just let me, let me buy it
Speaker 1: and go on with my life. Is that cool with you?
Speaker 1: But trying to push updates out onto devices that people
Speaker 1: that people you don't have act ys too, Like, that's
Speaker 1: not a fun place to be. Even from my days
Speaker 1: of supporting mobile apps, just trying to get people to
Speaker 1: update it was frustrating, all right, you know, thank you
Speaker 1: so much for joining us today. This has been a
Speaker 1: lot of fun.
Speaker 3: Likewise, I really enjoyed the composites.
Speaker 1: Orren as always, Thank you, appreciate you being on the show. Yeah,
Speaker 1: of course, and to all the listeners, thank you very
Speaker 1: very much because you're kind of the reason that we
Speaker 1: do this, so hopefully you enjoyed this. If not, you
Speaker 1: know how to find us and let us know, and
Speaker 1: you see always